FP_Group_25_CaDoD: Phase 4 - Cats vs Dogs Detector (CaDoD)

Loading Dependencies

!pip install torchviz # You have to run this code to get model visualization from torchviz make_dot

import torch
import torch.nn as nn
from torchsummary import summary
from torchviz import make_dot
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
from PIL import Image
import cv2
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import Dataset
from torchvision.transforms.functional import adjust_gamma

from sklearn import metrics
from sklearn import decomposition
from sklearn import manifold
from tqdm.notebook import trange, tqdm
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

import copy
import random
import time

from collections import Counter
import glob
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
from PIL import Image
from sklearn.preprocessing import StandardScaler
from sklearn.exceptions import ConvergenceWarning
from sklearn.metrics import accuracy_score, mean_squared_error, roc_auc_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
import tarfile
from tqdm.notebook import tqdm
import warnings
warnings.filterwarnings("ignore")

Connecting and loading images from Google Drive

# This section works only on the default Google Colab and not on Custom GCE VM or our local Jupyter Notebook
from google.colab import auth
from google.auth import default
from google.colab import drive
import gspread

'''
Authenticating against Google Sheets from the current Colab session
Google Snippets Colab Notebook:
https://colab.research.google.com/notebooks/snippets/sheets.ipynb
'''
auth.authenticate_user()
creds, _ = default()
gc = gspread.authorize(creds)


'''
Mounting Google Drive to the current Colab session
How to Connect Google Colab with Google Drive:
https://www.marktechpost.com/2019/06/07/how-to-connect-google-colab-with-google-drive/
'''
drive.mount('/content/drive')

Mounted at /content/drive

4.1 Team Summary

4.1.1 Team Meeting Schedule

We meet about 4 times per week. Due to our different schedules, we are not set on any specific days but we always pick the next meeting date&time based on how many tasks need to be reviewed and our availability.

Data below is based on TeamTable Google Sheet.

Our meetings so far:

'''
Loading content from a Google Sheets file and printing out the output
Google Snippets Colab Notebook:
https://colab.research.google.com/notebooks/snippets/sheets.ipynb
'''

# sheet 1 -> Leaders
# sheet 2 -> Meeting_Schedule
# sheet 3 -> Gantt_Chart
worksheet = gc.open('TeamTable').worksheet('Meeting_Schedule')   # Load only the "Meeting_Schedule" sheet from the TeamTable file

rows = worksheet.get_all_values()       # Get the list of all rows from a specific sheet

pd.set_option('display.max_colwidth', 0)
meetings = pd.DataFrame.from_records(rows[1:], columns=[rows[0]])    # Create a Pandas dataframe based on all the rows

meetings

	DATE	TIME	TOPICS
0	2023/03/22	4:00 PM	Introductions, logistics 1, project preferences, etc.
1	2023/03/26	6:00 PM	Picking the type of project, logistics 2, submitting Phase 0, starting Phase 1, etc.
2	2023/03/29	4:30 PM	Updates, organizing tasks, sharing resources, etc.
3	2023/03/31	9:00 PM	Updates, reviewing&helping with current tasks
4	2023/04/02	10:00 AM	Updates, wrapping up current tasks
5	2023/04/03	8:00 PM	Updates, finalizing Phase 1
6	2023/04/04	6:00 PM	Submitting Phase 1, dividing our work on classification and regression models
7	2023/04/08	5:00 PM	Phase 2 status update, classification and regression models progress, creating cloud environment, starting with the report, etc.
8	2023/04/09	7:00 PM	Phase 2 status update
9	2023/04/10	5:00 PM	Phase 2 status update, working on report, final hyperparameter tuning in the cloud, etc.
10	2023/04/11	6:00 PM	Wrapping up and submitting Phase 2, dividing our work for Phase 3
11	2023/04/14	8:00 PM	Phase 3 status update, sharing issues with PyTorch, coordinating tasks, etc.
12	2023/04/15	10:00 AM	Phase 3 status update, standardizing pre-processing and model creation approach, etc.
13	2023/04/16	2:00 PM	Phase 3 status update
14	2023/04/17	6:00 PM	Phase 3 status update, wrapping up and testing code, working on presentation, etc.
15	2023/04/18	6:00 PM	Wrapping up and submitting Phase 3, dividing our work for Phase 3
16	2023/04/20	7:00 PM	Preparation for presentation during Friday's lab session, dividing our work for Phase 4
17	2023/04/23	10:00 AM	Phase 4 status update
18	2023/04/24	5:00 PM	Phase 4 status update, wrapping up and testing code, working on presentation, etc.
19	2023/04/25	6:00 PM	Wrapping up and submitting Phase 4, dividing our work for Phase 3

4.1.2 Phase Leader Plan

We have decided Phase leader will be responsible for these tasks (either doing them or delegating them):

• Scheduling Zoom meetings
• Reminding us about meetings (Discord or Canvas notifications)
• Running meetings
• Ensuring we finish all critical tasks
• Adds and updates tasks
• Submits the Phase assignment(s)
• Creates and submits phase presentation

Data below is based on TeamTable Google Sheet.

'''
Loading content from a Google Sheets file and printing out the output
Google Snippets Colab Notebook:
https://colab.research.google.com/notebooks/snippets/sheets.ipynb
'''

# sheet 1 -> Leaders
# sheet 2 -> Meeting_Schedule
# sheet 3 -> Gantt_Chart
worksheet = gc.open('TeamTable').worksheet('Leaders')     # Load only the "Leaders" sheet from the TeamTable file

rows = worksheet.get_all_values()       # Get the list of all rows from a specific sheet

pd.set_option('display.max_colwidth', 0)
team = pd.DataFrame.from_records(rows[1:], columns=[rows[0]])         # Create a Pandas dataframe based on all the rows

team

	LEADER	PHASE
0	Vicente De Leon	1
1	Kelly Craig	2
2	Courtney Payton	3
3	Martin Berth	4

4.1.3 Credit/Goals

Legend:

• Phase - Identifies which phase the task is primarily for
• Task - Short task identifier
• End Date - This is the Due Date for a particular task
• Assignee - Person or persons responsible for completing or delegating particular task
• Credit - Person or persons who were involved in a particular task
• Description - Long task description

Data below is based on TeamTable Google Sheet.

'''
Loading content from a Google Sheets file
Google Snippets Colab Notebook:
https://colab.research.google.com/notebooks/snippets/sheets.ipynb
'''

# sheet 1 -> Leaders
# sheet 2 -> Meeting_Schedule
# sheet 3 -> Gantt_Chart
worksheet = gc.open('TeamTable').worksheet('Gantt_Chart')     # Load only the "Gantt_Chart" sheet from the TeamTable file

rows = worksheet.get_all_values()       # Get the list of all rows from a specific sheet
pd.set_option('display.max_colwidth', 0)
pd.set_option('display.max_rows', 500)

goals = pd.DataFrame.from_records(rows[6:], columns=[rows[5]])         # Create a Pandas dataframe based on all the rows

goals = goals[['PHASE', 'TASK', 'ASSIGNEE', 'CREDIT', 'DESCRIPTION']]   # Select only relevant columns
goals.columns = ['PHASE', 'TASK', 'ASSIGNEE', 'CREDIT', 'DESCRIPTION']   # Correct the columns

# Ignore columns with "Phase n" and hide Panda's index column
display(goals.loc[~goals['TASK'].isin(['Phase 0', 'Phase 1', 'Phase 2' ,'Phase 3', 'Phase 4'])].style.hide(axis="index"))

PHASE	TASK	ASSIGNEE	CREDIT	DESCRIPTION
0	Team logistics	Vicente	Courtney, Kelly, Martin, Vicente	We will decide on the team logistics and create them (creating a zoom room for meetings, creating Discord server for communication, creating a shared Google Drive location, selecting leaders for each week, etc.). We will individually research which project type we prefer to work on. Finally we will decide on the type of project.
0	Submitting Phase 0	Martin	Courtney, Kelly, Martin, Vicente	We will meet and Martin will submit the Phase 0 assignment while sharing his screen.
0	Google Colab testing	Vicente	Martin, Vicente	We will test how to use Google Colab. We will test sharing data between an instance of Google Colab notebook and shared Google Drive directory. We will research paid versions of Google Colab which include GPU for future project phases.
1	Research importing tables into Colab	Vicente	Vicente	We will research and test inserting data for Leaders, Credit, and Goals as tables into a testing Google Colab notebook for proposal for Phase 1.
1	Test running notebooks on our systems	Martin	Courtney, Kelly, Martin, Vicente	Since training models in paid Colab can become expensive with time, we will ensure we all can run our code both in Google Colab and locally on our workstations (this will be especially interesting with 3 different OS types and x64 and Apple M CPUs) . We will verify if the provided Docker container is suitable for running CPU/GPU intensive notebooks or if we will need to use Jupyter notebooks directly on our workstations.
1	Create goals in Google Sheets	Martin	Martin	We will finish scoping goals in Google Doc and will move them into Google Sheets file. We will ensure they are presentable for Leaders, Credit, and Goal sections in the proposal for Phase 1.
1	Provide a baseline coding notebook (local and Colab)	Courtney	Courtney	We will have a starting code for testing both local Jupyter notebook and Google Colab.
1	Baseline classification pipeline (SKLearn)	Vicente	Vicente, Courtney	We will research a baseline classification pipeline in SKLearn. We will also describe it for the proposal in Phase 1.
1	Baseline regression pipeline (SKLearn)	Martin	Martin	We will research a baseline regression pipeline in SKLearn. We will also describe it for the proposal in Phase 1.
1	Create Colab notebook for Phase 1 submission	Courtney	Kelly, Courtney	We will create a Google Colab notebook for the Phase 1 submission. It should include current requirements from the Phase 1 Assignment page.
1	Create baseline pipeline	Vicente	Vicente	We will put together the previous pipeline tasks and will follow up with TAs if the "baseline pipeline" requirement is satisfied.
1	Create diagrams and graphs	Vicente	Vicente, Martin	We will create diagrams describing the pipelines for the proposal. We will create several graphs describing data.
1	Create Gantt Chart	Kelly	Kelly	Using matplotlib we will create a Gantt chart based on the TeamTable spreadsheet. If the automated graph generation will not work, we will use Excel or some other tool and take a screenshot of the result.
1	Write an abstract	Kelly	Kelly	We will write an abstract for the proposal.
1	Prepare data description and EDA	Kelly	Kelly, Martin	We will describe data, provide basic EDA, and include previously generated graphs.
1	Pipeline and ML description	Courtney	Courtney	Describe algorithms and their implementations, metrics, loss functions and equations
1	Create list of previous meetings in Google Sheets	Martin	Courtney	We will have a list of previous meetings in Google Sheets. We should keep the list updated with any future meetings.
1	Insert data from Google Sheets into Google Colab	Martin	Martin, Vicente	We will insert the required proposal sections (Leaders, Credits, and Goals) from their sections in the proposal in a table form.
1	Submit the Phase 1 proposal into the discussion	Vicente	Vicente	We will create a PDF for the discussion (part of Phase 1 requirement). Since it is a Canvas discussion and it is supposed to be submitted only by one person from the team, we might want to do this together and the person submitting the discussion will be screen sharing with others since others will not have access to it.
1	Submit Phase 1 assignment	Vicente	Vicente	We will submit the Phase 1 assignment before the deadline.
2	Test custom GCE VM for Colab	Martin	Martin, Vicente	Since we decided to gain more experience with running our code in the cloud, we will create a custom dedicated GCE virtual machine for Google Colab and compare its performance with Google Colab Pro version. We will need to ensure we can still save data into our shared directory in Google Drive.
2	EDA and data metrics	Kelly	Kelly, Martin	We will create more detailed EDA and data metrics for the Phase 2 assignment.
2	Create baseline pipeline for classification and regression	Vicente	Kelly, Vicente	We will use the baseline pipelines designed in the Phase 1 and describe them in a brief report.
2	Research pre-processing (grayscale and HOG)	Vicente	Vicente	We will research using grayscale and HOG and their potential for pre-processing and feature engineering.
2	Feature engineering and selection research	Courtney	Vicente	We will finalize our feature engineering and selection and will discuss with TA if our approach makes sense.
2	Create classification pipeline (SKLearn)	Courtney and Vicente	Vicente, Martin	We will build an image classification model (using a pipeline and SKLearn).
2	Create regression pipeline (SKLearn)	Kelly, Vicente and Martin	Kelly, Courtney	We will build a regression model (using a pipeline and SKLearn) with 4 target values [y_1, y_2, y_3, y_4] corresponding to the bounding box containing the object of interest.
2	Hyperparameter tuning	Martin	Martin, Vicente, Kelly	We will do a hyperparameter tuning in a pipeline form for both classification and regression models.
2	Homegrown detector pipeline research	Courtney	Courtney	If we still have time available (if not, we will be addressing this in PyTorch in the next phase). We will implement a homegrown linear regression model with 4 target values. The MSE loss function will be extended from 1 to 4 targets (based on the coordinates of the bounding box).
2	Create Colab notebook for Phase 2 submission	Courtney	Courtney	If we still have time available (if not, we will be addressing this in PyTorch in the next phase). Based on the previous stretch goal, we will implement a homegrown logistic regression mode which will use a combination of CXE and MSE as a multi-task loss function where the resulting model predicts class and coordinates of the bounding box.
2	Prepare, create, and record a presentation for Phase 2	Kelly	Courtney, Kelly, Martin, Vicente	We will prepare a slide deck for presentation (under 300 words), review it, record it on Zoom (under 2 minutes long), and share it according to the requirements.
2	Submit Phase 2 presentation to the discussion board	Kelly	Kelly	We will submit the presentation and any additional requirements to the discussion board.
2	Submit Phase 2 assignment	Kelly	Kelly	We will submit the Phase 2 assignment before the deadline.
2	Learn and gain familiarity with pytorch functions	Courtney	Courtney, Martin	We will continue learning PyTorch, especially functions within PyTorch.
2	Research CNN	Courtney	Kelly	If we have time, we will research how we could use CNN in SKLearn and PyTorch.
2	Research pytorch model with MLP	Courtney	Courtney	If we have time, we will research how to use pytorch models using a MLP
3	AlexNet classification	Courtney	Courtney	We will build a classification model using AlexNet.
3	PyTorch digit detector classification model	Vicente	Vicente	We will build a PyTorch classification model using a multilayer perceptron.
3	PyTorch object detector regression model	Kelly	Kelly	We will build a PyTorch regression model using a multilayer perceptron with 4 target values [y_1, y_2, y_3, y_4] corresponding to the bounding box containing the object of interest.
3	Multi-headed object detector	Martin	Martin	We will build a multi-headed cat-dog detector using the OOP API in PyTorch with a combined loss function: CXE + MSE.
3	CNN for classification	Martin	Martin	We will build a baseline pipeline in PyTorch to do an object classification and object localization (predict the bounding box that contains the main object of interest in the image).
3	Prepare, create, and record a presentation for Phase 3	Courtney	Courtney, Kelly, Martin, Vicente	We will prepare a slide deck for presentation (under 300 words), review it, record it on Zoom (under 2 minutes long), and share it according to the requirements.
3	Submit Phase 3 presentation to the discussion board	Courtney	Courtney	We will submit the presentation and any additional requirements to the discussion board.
3	Submit Phase 3 assignment	Courtney	Courtney	We will submit the Phase 3 assignment before the deadline.
4	Research transfer learning	Vicente and Courtney	Kelly, Courtney	We will research transfer learning for object detection and fine-tune it using EfficientNet (D0-D7) for object detection. We will be able to describe architecture and loss function for EfficientDet. We will describe the architecture and loss functions of EfficientDet. We will describe differences between EfficientDet D0 and EfficientNet D7.
4	Implement transfer learning(EfficientNet)	Courtney Vicente	Kelly	We will implement transfer learning for object detection based on the previous research task.
4	Alexnet Improvements	Kelly and Martin	Courtney	Fine tuning AlexNet Model that was built in phase 3 with a goal of at least 70% accuracy
4	Build FCN	Martin	Martin	We will create a convolutional neural network (FCN) for a single object classifier and detector.
4	Build CNN	Vicente	Vicente	We will create a convolutional neural network (CNN) for image classification.
4	Prepare, create, and record a presentation for Phase 4	Martin	Martin, Vicente, Kelly, Courtney	We will prepare a slide deck for presentation (under 300 words), review it, record it on Zoom (under 2 minutes long), and share it according to the requirements.
4	Submit Phase 4 presentation to the discussion board	Martin	Martin	We will submit the presentation and any additional requirements to the discussion board.
4	Submit Phase 4	Martin	Martin	We will submit the Phase 4 assignment before the deadline.

4.1.4 Gantt Chart

This chart represents our currently planed schedule which is based on our current best estimates and likely will change.

# Code to create dataframe for Gantt Chart table
'''
How to get a Gantt plot using matplotlib?:
https://www.tutorialspoint.com/how-to-get-a-gantt-plot-using-matplotlib
'''

gc = gspread.authorize(creds)
worksheet = gc.open('TeamTable').worksheet('Gantt_Chart')   # Load only the "Gantt_Chart" sheet from the file
rows = worksheet.get_all_values() # get_all_values gives a list of rows

pd.set_option('display.max_colwidth', 0)
# Create dataframe
gantt = pd.DataFrame.from_records(rows[7:], columns=[rows[5]])
# Keep first 3 columns
gantt = gantt.iloc[:, 1:4]
# Give column names
gantt.columns = ['TASK','START DATE','END DATE']
# Drop Phase rows
index_num = gantt[(gantt['TASK'] == 'Phase 0')|(gantt['TASK'] == 'Phase 1')|(gantt['TASK'] == 'Phase 2')|(gantt['TASK'] == 'Phase 3')|(gantt['TASK'] == 'Phase 4')].index
gantt = gantt.drop(index_num)
# gantt

# Create a gantt chart

# convert to datetime
gantt['START DATE']= pd.to_datetime(gantt['START DATE'])
gantt['END DATE']= pd.to_datetime(gantt['END DATE'])

# Create new columns
gantt['days_to_start'] = (gantt['START DATE'] - gantt['START DATE'].min()).dt.days
gantt['days_to_end'] = (gantt['END DATE'] - gantt['START DATE'].min()).dt.days
gantt['task_duration'] = gantt['days_to_end'] - gantt['days_to_start'] + 1  # to include also the end date
gantt['Phase'] = ['Phase 0'] * 3 + ['Phase 1'] * 17 + ['Phase 2'] * 16 + ['Phase 3'] * 8 + ['Phase 4'] * 8

# Create Gantt Chart
team_colors = {'Phase 0': 'g', 'Phase 1': 'c', 'Phase 2': 'm', 'Phase 3': 'y', 'Phase 4': 'b'} # dictionary with the team names as its keys and base matplotlib colors as its values
fig, ax = plt.subplots()
fig.set_size_inches(12, 18)

for index, row in gantt.iterrows():
  plt.barh(y=row['TASK'], width=row['task_duration'], left=row['days_to_start'], color=team_colors[row['Phase']])

plt.title('Final Project Gantt Chart', fontsize=15)
plt.gca().invert_yaxis()
xticks = np.arange(0, gantt['days_to_end'].max()+2, 2) 
xticklabels = pd.date_range(start=gantt['START DATE'].min(), end=gantt['END DATE'].max()).strftime("%d/%m")

# ticks
ax.set_xticks(xticks)

ax.set_xticklabels(xticklabels[::2])
# axis
ax.xaxis.grid(True, alpha=0.5) 
# Adding a legend
patches = []
for team in team_colors:
    patches.append(matplotlib.patches.Patch(color=team_colors[team]))
ax.legend(handles=patches, labels=team_colors.keys(), fontsize=11) 
plt.show()

4.2 Project Abstract

The goal of this project is to create optimal cat and dog image detection models. In the first phase of this project, we created baseline SKLearn pipelines using classification models to predict cat or dog labels and regression models to predict the image bounding box. In phase 2 of this project, we utilized pytorch to create baseline classification and regression neural networks. In addition, we implemented hyperparameter tuning, gray scale, image augmentation and HOG feature engineering steps to improve our models performance.In phase 3, we continued using image augmentation and we constructed multilayer perceptron models for classification and regression using Pytorch. In the final phase of this project, we created complex neural networks such as Convolutional Neural Network and Fully Convolutional Network using Keras and Tensorflow. In addition, we implemented transfer learning with AlexNet and EfficientNet. EfficientNetB5 was our top performing model with a test accuracy score of 99.4%.

4.3 Project Description

In the fourth and final phase of our project, we planned to utilize our successes and failures from prior submissions as a learning benchmark in order to improve the overall cat and dog image detection goal. We hoped to achieve accuracy scores above 70% and far surpassed that metric. Within this phase, we will be implementing several classification methods including a convolutional neural network(CNN), a fully convolutional neural network (FCN) for single object detection and classification, and methods of transfer learning utilizing EfficientNet(0-5) and Alexnet.

CNN: Pictured below is the architecture that will be used to implement the CNN model for object detection. This model will be built with an input structure size of (128,128,3) via a reshape method. Augmentation and preprocessing steps will be applied to prevent overfitting and the model will be fine tuned through testing results of different optimizers of Adam vs. RMSprop. Results will be displayed using tensorboard.

from IPython.display import Image, display
colab_path = '/content/drive/MyDrive/aml/'
display(Image(colab_path + 'Pictures/CNN_model.png'))

FCN: As seen below, the input size for the fully convolutional neural network(FCN) will be (128,128,3). FCN will be built similarly to the CNN model in terms of augmentation and preprocessing. This model will be fine tuned by testing optimizers of stochastic gradient descent (SGD) and Adam, adjusting learning rate, and batch sizes until best results are achieved. Results will be displayed using tensorboard.

display(Image(colab_path + 'Pictures/FCN_model.png'))

EfficientNet(0-5): For the Efficient family of models, the input parameters (such as width, depth, and resolution) increase from EfficientNetB0 to EfficientNetB7. The image resolution will increase from 224 for EfficientNetB0 to an image resolution of 600 for EfficientNetB7. RMSprop optimizer will be implemented resulting in an increase of parameters with each application. The architectural framework can be seen below.

display(Image(colab_path + 'Pictures/EN_Model.png'))

Alexnet: Seen below we can visualize the architectural network utilized in the Alexnet model framework. The goal for this phase was to improve results in the Alexnet model from phase 3 by applying the reshape method to change the input to the size required for Alexnet. This will be done by utilizing a different data set, defining a reshape model, and fine tuning with different optimizers and learning rates until optimal results are achieved. Overall, this should receive better accuracy and loss results along with increasing the number of epochs.

display(Image(colab_path + 'Pictures/Alex_Model.png'))

Accuracy Equation: $${Accuracy} = \dfrac {c}{n} $$

Notation: \begin{align} c &\quad \text{Number of correct predictions}\\ n &\quad \text{Total number of predictions}\\ \end{align}

MSE Equation: Loss function that works for multiple alogorithms including logistic regression

\begin{equation}\tag{Reference: Lab 4 Linear Regression} MSE(\boldsymbol{\theta}) = f(\boldsymbol{\theta}) = \frac{1}{m}\sum_{i=1}^{m}\left[ \mathbf{x}_i\cdot\boldsymbol{\theta} - y_i\right]^2 \end{equation}

Notation: \begin{align} m &\quad \text{Number of examples in data set}\\ \mathbf{x}_i &\quad \text{input variable}\\ (\boldsymbol{\theta}) &\quad \text{model's parameter vector}\\ y &\quad \text{target class}\\ \end{align}

RMSE Equation Root mean squared error function is just the square root of the mean squared error

\begin{align}\tag{Reference: Lab 4 Linear Regression} \text{RMSE}(\mathbf{X}, h_{\mathbf{\theta}}) = \sqrt{\dfrac{1}{m} \sum\limits_{i=1}^{m}{( \mathbf{x}^{(i)}\cdot \mathbf{\theta} - y^{(i)})^2}} \end{align}

Notation: \begin{align} m &\quad \text{Number of examples in data set}\\ \mathbf{x}_i &\quad \text{input variable}\\ (\boldsymbol{\theta}) &\quad \text{model's parameter vector}\\ y &\quad \text{target class}\\ \end{align}

MAE Equation \begin{align}\tag{Reference: Lab 4 Linear Regression} \text{MAE}(\mathbf{X}, h_{\mathbf{\theta}}) = \dfrac{1}{m} \sum\limits_{i=1}^{m}{| \mathbf{x}^{(i)}\cdot \mathbf{\theta} - y^{(i)}|} \end{align}

Notation:

\begin{align} m &\quad \text{Number of examples in data set}\\ \mathbf{x}_i &\quad \text{input variable}\\ (\boldsymbol{\theta}) &\quad \text{model's parameter vector}\\ y &\quad \text{target class}\\ \end{align}

BCE Equation: Binary Cross Entropy loss function \begin{align} \tag{Reference: Lab 7 Logistic Regression} \mathcal{L}_{\text{BCE}}(y, \hat{y}) = -\frac{1}{N}\sum_{i=1}^{N} y_i \log(\hat{y_i}) + (1 - y_i) \log(1 - \hat{y_i}) \end{align} Notation: \begin{align} N &\quad \text{number of classes}\\ y &\quad \text{target class}\\ \hat{y} &\quad \text{predicted classes}\\ \end{align}

Confusion Matrix Equations For confusion matrices, we will be using precision and F-1 score

Notation: \begin{align} TP &\quad \text{True Positives in matrix}\\ TN &\quad \text{True Negatives in matrix}\\ FP &\quad \text{False Positives in matrix}\\ FN &\quad \text{False Negatives in matrix}\\ \end{align}

Precision

\begin{align} \text{Precison} = \dfrac{TP}{TP+FP} \end{align}

F1 Score

\begin{align} \text{F1 Score} = 2\cdot\dfrac{Precision⋅Recall}{Precision+Recall} \end{align}

Where: \begin{align} \text{Recall} = \dfrac{TP}{TP+FN} \end{align}

4.3.1 Data Description

The data set for this project is a subset of Open Images v6 and it contains 12,966 RGB images of cats and dogs with various shapes and aspect ratios. In addition, data containing image bounding box coordinates of the object are stored in a .csv file. There are 6,855 data points classified as dogs and 6,111 as cats. The image bounding box file contains the following information:

• ImageID - the image ID
• Source - How the bounding box was created.
  • xclick - manually drawn boxes
  • activemil - enhanced method and human verified.
• LabelName - object class ID
• Confidence - dummy value of 1
• XMin, XMax, YMin, YMax - coordinates of the box, in normalized image coordinates. XMin is in [0,1], where 0 is the leftmost pixel, and 1 is the rightmost pixel in the image. Y coordinates go from the top pixel (0) to the bottom pixel (1).
• IsOccluded - 1 if the object is occluded by another object in the image.
• IsTruncated - 1 if the object is trunacated in the image.
• IsDepiction - 1 if the object is not a real physical instance.
• IsInside - 1 if the picture taken from the inside of a object
• XClick1X, XClick2X, XClick3X, XClick4X, XClick1Y, XClick2Y, XClick3Y, XClick4Y - normalized image coordinates of the four extreme points of the object that produced the box.

4.3.2 Exploratory data analysis (EDA)

The code below reads the dataset and loads it into memory.

# Loading Variables
# These 2 lines below can be changed based on what you want to run:
dataset = 'cadod.csv'                             # cadod.csv  or cadod_experimental.csv
gdrive_directory = 'aml'           # This is your personal GDrive directly. For example in Vicente's case: MLProject

################################
# You do not need to edit these variables
colab_path = '/content/drive/MyDrive/' + gdrive_directory + '/aml'
images_path = colab_path + '/images'
# images_resized_path = images_path + '/resized'

# df = pd.read_csv(colab_path + '/' + dataset)
df = pd.read_csv(colab_path + '/' + dataset)

# Image Bounding Box File

# List the first 5, last 5, and random 5 rows from the Bounding Box File. This gives us a quick overview of the data.
display(df.head())
print()
display(df.tail())
print()
display(df.sample(n=5))

	ImageID	Source	LabelName	Confidence	XMin	XMax	YMin	YMax	IsOccluded	IsTruncated	...	IsDepiction	IsInside	XClick1X	XClick2X	XClick3X	XClick4X	XClick1Y	XClick2Y	XClick3Y	XClick4Y
0	0000b9fcba019d36	xclick	/m/0bt9lr	1	0.165000	0.903750	0.268333	0.998333	1	1	...	0	0	0.636250	0.903750	0.748750	0.165000	0.268333	0.506667	0.998333	0.661667
1	0000cb13febe0138	xclick	/m/0bt9lr	1	0.000000	0.651875	0.000000	0.999062	1	1	...	0	0	0.312500	0.000000	0.317500	0.651875	0.000000	0.410882	0.999062	0.999062
2	0005a9520eb22c19	xclick	/m/0bt9lr	1	0.094167	0.611667	0.055626	0.998736	1	1	...	0	0	0.487500	0.611667	0.243333	0.094167	0.055626	0.226296	0.998736	0.305942
3	0006303f02219b07	xclick	/m/0bt9lr	1	0.000000	0.999219	0.000000	0.998824	1	1	...	0	0	0.508594	0.999219	0.000000	0.478906	0.000000	0.375294	0.720000	0.998824
4	00064d23bf997652	xclick	/m/0bt9lr	1	0.240938	0.906183	0.000000	0.694286	0	0	...	0	0	0.678038	0.906183	0.240938	0.522388	0.000000	0.370000	0.424286	0.694286

5 rows × 21 columns

	ImageID	Source	LabelName	Confidence	XMin	XMax	YMin	YMax	IsOccluded	IsTruncated	...	IsDepiction	IsInside	XClick1X	XClick2X	XClick3X	XClick4X	XClick1Y	XClick2Y	XClick3Y	XClick4Y
12961	ffc65ce637cbd73d	xclick	/m/01yrx	1	0.135000	0.844375	0.000000	0.999167	0	1	...	0	0	0.176875	0.135000	0.450625	0.844375	0.000000	0.685000	0.999167	0.995000
12962	ffd1e6a91d92bf83	xclick	/m/01yrx	1	0.000625	0.999375	0.005000	0.825000	1	1	...	0	0	0.220625	0.125000	0.000625	0.999375	0.825000	0.005000	0.261667	0.303333
12963	ffe91ea1debeefb3	xclick	/m/01yrx	1	0.001475	0.988201	0.042406	0.624260	1	1	...	0	0	0.473451	0.001475	0.019174	0.988201	0.042406	0.327416	0.624260	0.358974
12964	ffebb214b9df34aa	xclick	/m/01yrx	1	0.000000	0.998125	0.037523	0.999062	0	1	...	0	0	0.000000	0.399375	0.998125	0.581250	0.676360	0.037523	0.560976	0.999062
12965	fffcbea446a0b7b9	xclick	/m/01yrx	1	0.148045	0.999069	0.070640	0.947020	0	1	...	0	0	0.558659	0.148045	0.808194	0.999069	0.070640	0.370861	0.947020	0.835541

5 rows × 21 columns

	ImageID	Source	LabelName	Confidence	XMin	XMax	YMin	YMax	IsOccluded	IsTruncated	...	IsDepiction	IsInside	XClick1X	XClick2X	XClick3X	XClick4X	XClick1Y	XClick2Y	XClick3Y	XClick4Y
4337	9d22da1e9caa418e	xclick	/m/0bt9lr	1	0.000000	0.893750	0.000000	0.999163	0	1	...	0	0	0.120625	0.000000	0.893750	0.561875	0.000000	0.184937	0.897908	0.999163
5185	bc9770bf4c2d4121	xclick	/m/0bt9lr	1	0.001250	0.915625	0.000000	0.868333	0	1	...	0	0	0.290625	0.001250	0.860625	0.915625	0.000000	0.527500	0.868333	0.746667
6979	0122ed068d6d0d40	xclick	/m/01yrx	1	0.181250	0.880625	0.067500	0.902500	0	0	...	0	0	0.307500	0.880625	0.181250	0.367500	0.067500	0.504167	0.395000	0.902500
12797	f7e71b714f18a026	xclick	/m/01yrx	1	0.317647	0.998824	0.000000	0.998230	0	1	...	0	0	0.748235	0.998824	0.830588	0.317647	0.000000	0.495575	0.998230	0.631858
11892	ce6ee11659b2f914	xclick	/m/01yrx	1	0.181250	0.940625	0.060417	0.997917	1	1	...	0	0	0.206250	0.181250	0.496875	0.940625	0.060417	0.600000	0.997917	0.668750

5 rows × 21 columns

# Descriptive statistics
df.describe()

	Confidence	XMin	XMax	YMin	YMax	IsOccluded	IsTruncated	IsGroupOf	IsDepiction	IsInside	XClick1X	XClick2X	XClick3X	XClick4X	XClick1Y	XClick2Y	XClick3Y	XClick4Y
count	12966.0	12966.000000	12966.000000	12966.000000	12966.000000	12966.000000	12966.000000	12966.000000	12966.000000	12966.000000	12966.000000	12966.000000	12966.000000	12966.000000	12966.000000	12966.000000	12966.000000	12966.000000
mean	1.0	0.099437	0.901750	0.088877	0.945022	0.464754	0.738470	0.013651	0.045427	0.001157	0.390356	0.424582	0.494143	0.506689	0.275434	0.447448	0.641749	0.582910
std	0.0	0.113023	0.111468	0.097345	0.081500	0.499239	0.440011	0.118019	0.209354	0.040229	0.358313	0.441751	0.405033	0.462281	0.415511	0.401580	0.448054	0.403454
min	1.0	0.000000	0.408125	0.000000	0.451389	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000
25%	1.0	0.000000	0.830625	0.000000	0.910000	0.000000	0.000000	0.000000	0.000000	0.000000	0.221292	0.096875	0.285071	0.130000	0.024323	0.218333	0.405816	0.400000
50%	1.0	0.061250	0.941682	0.059695	0.996875	0.000000	1.000000	0.000000	0.000000	0.000000	0.435625	0.415625	0.531919	0.623437	0.146319	0.480838	0.825000	0.646667
75%	1.0	0.167500	0.998889	0.144853	0.999062	1.000000	1.000000	0.000000	0.000000	0.000000	0.609995	0.820000	0.787500	0.917529	0.561323	0.729069	0.998042	0.882500
max	1.0	0.592500	1.000000	0.587088	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.999375	0.999375	1.000000	0.999375	0.999375	0.999375	1.000000	0.999375

# Print a concise summary of the bounding box file. 
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12966 entries, 0 to 12965
Data columns (total 21 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   ImageID      12966 non-null  object 
 1   Source       12966 non-null  object 
 2   LabelName    12966 non-null  object 
 3   Confidence   12966 non-null  int64  
 4   XMin         12966 non-null  float64
 5   XMax         12966 non-null  float64
 6   YMin         12966 non-null  float64
 7   YMax         12966 non-null  float64
 8   IsOccluded   12966 non-null  int64  
 9   IsTruncated  12966 non-null  int64  
 10  IsGroupOf    12966 non-null  int64  
 11  IsDepiction  12966 non-null  int64  
 12  IsInside     12966 non-null  int64  
 13  XClick1X     12966 non-null  float64
 14  XClick2X     12966 non-null  float64
 15  XClick3X     12966 non-null  float64
 16  XClick4X     12966 non-null  float64
 17  XClick1Y     12966 non-null  float64
 18  XClick2Y     12966 non-null  float64
 19  XClick3Y     12966 non-null  float64
 20  XClick4Y     12966 non-null  float64
dtypes: float64(12), int64(6), object(3)
memory usage: 2.1+ MB

# Check for any NA values
df.isnull().sum()

ImageID        0
Source         0
LabelName      0
Confidence     0
XMin           0
XMax           0
YMin           0
YMax           0
IsOccluded     0
IsTruncated    0
IsGroupOf      0
IsDepiction    0
IsInside       0
XClick1X       0
XClick2X       0
XClick3X       0
XClick4X       0
XClick1Y       0
XClick2Y       0
XClick3Y       0
XClick4Y       0
dtype: int64

# Check data type of each column
df.dtypes

ImageID        object 
Source         object 
LabelName      object 
Confidence     int64  
XMin           float64
XMax           float64
YMin           float64
YMax           float64
IsOccluded     int64  
IsTruncated    int64  
IsGroupOf      int64  
IsDepiction    int64  
IsInside       int64  
XClick1X       float64
XClick2X       float64
XClick3X       float64
XClick4X       float64
XClick1Y       float64
XClick2Y       float64
XClick3Y       float64
XClick4Y       float64
dtype: object

# List how much memory each column uses in bytes.
# This could be useful if we were working with truly large data sets and memory would be a constrain.
df.memory_usage()

Index          128   
ImageID        103728
Source         103728
LabelName      103728
Confidence     103728
XMin           103728
XMax           103728
YMin           103728
YMax           103728
IsOccluded     103728
IsTruncated    103728
IsGroupOf      103728
IsDepiction    103728
IsInside       103728
XClick1X       103728
XClick2X       103728
XClick3X       103728
XClick4X       103728
XClick1Y       103728
XClick2Y       103728
XClick3Y       103728
XClick4Y       103728
dtype: int64

# List counts of unique values in each column. This is useful for determining if the variable is categorical.
df.nunique()

ImageID        12966
Source         2    
LabelName      2    
Confidence     1    
XMin           3109 
XMax           3499 
YMin           4822 
YMax           4219 
IsOccluded     3    
IsTruncated    3    
IsGroupOf      3    
IsDepiction    3    
IsInside       3    
XClick1X       5135 
XClick2X       4763 
XClick3X       5074 
XClick4X       4659 
XClick1Y       6351 
XClick2Y       7314 
XClick3Y       6252 
XClick4Y       7180 
dtype: int64

# Get the rows and columns of the bounding box data file.
print(f"There are {df.shape[0]} rows and {df.shape[1]} columns")

There are 12966 rows and 21 columns

# Number of dog vs cat labels.
# The counts of cats and dogs was close enough that we concluded not to do any sample balancing.
df.LabelName.replace({'/m/01yrx':'cat', '/m/0bt9lr':'dog'}, inplace=True)
df.LabelName.value_counts()

dog    6855
cat    6111
Name: LabelName, dtype: int64

4.3.3 Plot Images

# plot random 6 images
fig, ax = plt.subplots(nrows=2, ncols=3, sharex=False, sharey=False,figsize=(15,10))
ax = ax.flatten()

for i,j in enumerate(np.random.choice(df.shape[0], size=6, replace=False)):
    img = mpimg.imread(images_path + "/" + df.ImageID.values[j] + '.jpg')
    h, w = img.shape[:2]
    coords = df.iloc[j,4:8]
    ax[i].imshow(img)
    ax[i].set_title(df.LabelName[j])
    ax[i].add_patch(plt.Rectangle((coords[0]*w, coords[2]*h), 
                                  coords[1]*w-coords[0]*w, coords[3]*h-coords[2]*h, 
                                  edgecolor='red', facecolor='none'))

plt.tight_layout()
plt.show()

4.3.4 Bar Graph of Image Shape Counts

This is a bar plot of the all the image shape counts. The "other" category includes all of the image shapes that have a count of less than 100. 512x384 is the image size that occurs the most.

#Plot image shape counts
img_df.sort_values('img_count', inplace=True)
img_df.plot(x='img_shape', y='img_count', kind='barh', figsize=(8,8), legend=False)
plt.title('Image Shape Counts')
plt.show()

# convert to megabytes
img_size = img_size / 1000

4.3.5 Plots of Image Size Distribution

This is a histogram and box plot of image size (MB).

# Plot image size distribution
fig, ax = plt.subplots(1, 2, figsize=(15,5))
fig.suptitle('Image Size Distribution')
ax[0].hist(img_size, bins=50)
ax[0].set_title('Histogram')
ax[0].set_xlabel('Image Size (MB)')
ax[1].boxplot(img_size, vert=False, widths=0.5)
ax[1].set_title('Boxplot')
ax[1].set_xlabel('Image Size (MB)')
ax[1].set_ylabel('Images')
plt.show()

4.4 Loading data

colab_path = '/content/drive/MyDrive/MLProject/aml/data/martin'

X = np.load(colab_path + '/img.npy', allow_pickle=True)
y_label = np.load(colab_path + '/y_label.npy', allow_pickle=True)
y_bbox = np.load(colab_path + '/y_bbox.npy', allow_pickle=True)

4.5 CNN

4.5.1 Loading Dependencies

TensorBoard

import tensorflow as tf
import datetime

!pip install -q -U tensorboard
%load_ext tensorboard

Keras

!pip install livelossplot

from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.utils import plot_model
from livelossplot import PlotLossesKeras
from keras.callbacks import CSVLogger
from tensorflow.keras.callbacks import TensorBoard

Numpy, Pandas, Sklearn, Google Colab

import csv
import numpy as np
import random
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import os
import pandas as pd
from PIL import Image
from sklearn.exceptions import ConvergenceWarning
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings("ignore")

from google.colab import auth
from google.auth import default
from google.colab import drive
import gspread

auth.authenticate_user()
creds, _ = default()
gc = gspread.authorize(creds)
drive.mount('/content/drive')

Mounted at /content/drive

4.5.2 Data Preparation

#colab_path = '/content/drive/MyDrive/MLProject/aml/data/martin'

#X = np.load(colab_path + '/img.npy', allow_pickle=True)
#y_label = np.load(colab_path + '/y_label.npy', allow_pickle=True)
#y_bbox = np.load(colab_path + '/y_bbox.npy', allow_pickle=True)

X_train, X_test, y_train, y_test_label = train_test_split(X, y_label, test_size=0.2, random_state=42)

X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.2, random_state=42)

X_train.shape # lets check the shape

(8297, 49152)

X_test.shape # lets check the shape

(2594, 49152)

# https://www.w3schools.com/python/gloss_python_check_if_set_item_exists.asp

if set(y_train) == {0, 1}: # labels 0 and 1
    print("Labels are 0 and 1")
else:
    print("Labels are not 0 and 1")

Labels are 0 and 1

X_train = X_train.reshape((-1, 128, 128, 3)) # reshaping as before, but using channel 3 for RGB
X_valid = X_valid.reshape((-1, 128, 128, 3)) # reshaping as before, but using channel 3 for RGB
X_test = X_test.reshape((-1, 128, 128, 3)) # reshaping as before, but using channel 3 for RGB

num_classes = len(set(y_label))
y_train = to_categorical(y_train, num_classes)
y_valid = to_categorical(y_valid, num_classes)
y_test = to_categorical(y_test_label, num_classes)

4.5.3 Data Augmentation + ImageDataGenerator

Our data augmentation processes are a mix of the 3 sources below. We are setting up Keras ImageDataGenerator for the Training, Validation, and Testing set. We are applying a couple of basic data augmentation techniques to create data batches on the training set including normalization (rescale), rotation, shift (width and height), share range, zoom, horizontal flip, and fill mode. Both the Validation and Test sets are just being normalized. Keras ImageDataGenerator, '.fit()', and '.flow()' are used to set up the dataset.

# https://www.geeksforgeeks.org/python-data-augmentation/
# https://gsurma.medium.com/image-classifier-cats-vs-dogs-with-convolutional-neural-networks-cnns-and-google-colabs-4e9af21ae7a8
# https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator

train_generator = ImageDataGenerator(
    rescale=1./255,
    rotation_range=30,
    width_shift_range=0.1,
    height_shift_range=0.1,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest') # Will determine how the pixels are generator

valid_generator = ImageDataGenerator(rescale=1./255) # normalization
test_generator = ImageDataGenerator(rescale=1./255) # normalization

'''
We tried to use the code below for data augmentation techniques, however we found that the above code returns better results.
'''
#train_generator = ImageDataGenerator(
    #rescale=1./255,
    #rotation_range=45,
    #width_shift_range=0.2,
    #height_shift_range=0.2,
    #shear_range=0.3,
    #zoom_range=0.3,
    #horizontal_flip=True,
    #fill_mode='nearest',
    #brightness_range=[0.5, 1.5])

#valid_generator = ImageDataGenerator(rescale=1./255)
#test_generator = ImageDataGenerator(rescale=1./255)

train_generator.fit(X_train) # applying fit
valid_generator.fit(X_valid) # applying fit
test_generator.fit(X_test) # applying fit

# https://studymachinelearning.com/keras-imagedatagenerator-with-flow/

batch = 32 # Keras batch size default.

train_generator = train_generator.flow(X_train, y_train, batch_size=batch) # applying Keras .flow()
valid_generator = valid_generator.flow(X_valid, y_valid, batch_size=batch) # applying Keras .flow()
test_generator = test_generator.flow(X_test, y_test, batch_size=batch) # applying Keras .flow()

4.5.4 Creating Keras CNN Model for Cat and Dog Classification

The following CNN Model came from the tutorial provided below. This Model uses Keras Sequential API and had many layers like Conv2D (convolutional layers), MaxPooling2D (max pooling for 2D spatial data), and Dense (fully connected). It is important to state that our input size will be 128x128x3, just like we reshaped our data initially. It also consists of:

• Padding = ‘same’. Meaning it will make output size the same size as the input.
• Actividation = relu. Relu activiation function just like Phase 3.
• Flatten() will flatten the input.
• Dropout() for regularization purposes to avoid cases of overfitting.
• Loss = binary cross entropy. Probabilities vs True Labels
• Optimizer = Adam or any other.
• Metric = Accuracy.

Relu Activation Function (Rectified Activation Function): With default values, this returns the standard ReLU activation: max(x, 0), the element-wise maximum of 0 and the input tensor.

$$ReLU(x) = (x)^+ = \max(0, x)$$

Softmax: Used for the classification network. It is mostly used for the last layer of a classification network.

$$\text{softmax }(\mathbf{x}) = \frac{e^{x_i}}{\sum_j e^{x_j}}$$

Binary Cross Entropy: Computes the cross-entropy loss between true labels and predicted labels.

$$\mathcal{L}_{\text{BCE}}(y, \hat{y}) = -\frac{1}{N}\sum_{i=1}^{N} y_i \log(\hat{y_i}) + (1 - y_i) \log(1 - \hat{y_i})$$

We decided to use two dimensional convolutional layers to try and extract the most relevant features from the images. We do this by applying filters to detect and specific feature in the image. The max pooling layers will reduce dimensionality while retaining the most important features as well. The flatten layer is used to convert features into 1 dimensional vector (used specifically for classification tasks). Dense layers perform classification tasks by computing weighted sum of the input images while applying the activation function, in this case ReLU. The final dense layer uses SoftMax activation function for probability purposes.

Sources:

• https://gsurma.medium.com/image-classifier-cats-vs-dogs-with-convolutional-neural-networks-cnns-and-google-colabs-4e9af21ae7a8

• https://keras.io/api/layers/convolution_layers/convolution2d/

• https://keras.io/api/layers/pooling_layers/max_pooling2d/

• https://wandb.ai/krishamehta/seo/reports/Difference-Between-SAME-and-VALID-Padding-in-TensorFlow--VmlldzoxODkwMzE

• https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax

• https://keras.io/api/layers/reshaping_layers/flatten/

• https://machinelearningmastery.com/dropout-regularization-deep-learning-models-keras/

from tensorflow.keras.optimizers import Adam # Optimizer
#from tensorflow.keras.optimizers import RMSprop 

'''
Model from tutorial: 
https://gsurma.medium.com/image-classifier-cats-vs-dogs-with-convolutional-neural-networks-cnns-and-google-colabs-4e9af21ae7a8
'''


model = Sequential()

model.add(Conv2D(32, (3, 3), padding='same', input_shape=(128, 128, 3), activation='relu'))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))

model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))

model.add(Dense(num_classes, activation='softmax'))
    
model.compile(loss='binary_crossentropy',
            optimizer=Adam(learning_rate=0.0001),
            metrics=['accuracy'])

4.5.4.1 Model Structure

# https://machinelearningmastery.com/visualize-deep-learning-neural-network-model-keras/
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 128, 128, 32)      896       
                                                                 
 conv2d_1 (Conv2D)           (None, 128, 128, 32)      9248      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 64, 64, 32)       0         
 )                                                               
                                                                 
 conv2d_2 (Conv2D)           (None, 64, 64, 64)        18496     
                                                                 
 conv2d_3 (Conv2D)           (None, 64, 64, 64)        36928     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 32, 32, 64)       0         
 2D)                                                             
                                                                 
 conv2d_4 (Conv2D)           (None, 32, 32, 128)       73856     
                                                                 
 conv2d_5 (Conv2D)           (None, 32, 32, 128)       147584    
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 16, 16, 128)      0         
 2D)                                                             
                                                                 
 conv2d_6 (Conv2D)           (None, 16, 16, 256)       295168    
                                                                 
 conv2d_7 (Conv2D)           (None, 16, 16, 256)       590080    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 8, 8, 256)        0         
 2D)                                                             
                                                                 
 flatten (Flatten)           (None, 16384)             0         
                                                                 
 dense (Dense)               (None, 256)               4194560   
                                                                 
 dropout (Dropout)           (None, 256)               0         
                                                                 
 dense_1 (Dense)             (None, 256)               65792     
                                                                 
 dropout_1 (Dropout)         (None, 256)               0         
                                                                 
 dense_2 (Dense)             (None, 2)                 514       
                                                                 
=================================================================
Total params: 5,433,122
Trainable params: 5,433,122
Non-trainable params: 0
_________________________________________________________________

# https://machinelearningmastery.com/visualize-deep-learning-neural-network-model-keras/
plot_model(model, to_file='model.png', show_shapes=True)

4.5.4.2 Training

Our training is going to consist of 25 epochs to see how far our CNN model can learn. Just like the tutorial, the training section produces a training log csv that we are going to use for dataframe creation as well as displaying the max values for accuracy and loss. For this task, we are going to visualize training results in real time using TensorBoard. It is worth mentioning that we took inspiration from other sources to incorporate ‘log_dir’ which gives us the time/date and ‘tensorboard_callback’ that helps us visualize results in TensorBoard. No special rule regarding epoch range, we wanted a range between 20 and 30 (even though we used 20 back in Phase 3).

# Delete any logs from previous runs
!rm -rf ./logs/

#https://gsurma.medium.com/image-classifier-cats-vs-dogs-with-convolutional-neural-networks-cnns-and-google-colabs-4e9af21ae7a8
#https://machinelearningmastery.com/display-deep-learning-model-training-history-in-keras/
#https://www.tensorflow.org/tensorboard/get_started

epochs = 25

csv_log = 'Adamtraining_logs.csv'

log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
%tensorboard --logdir logs/fit

history = model.fit_generator(
    train_generator,
    steps_per_epoch=len(X_train) // batch,
    epochs=epochs,
    validation_data=valid_generator,
    validation_steps=len(X_valid) // batch,
    callbacks=[CSVLogger(csv_log, append=False, separator=';'), tensorboard_callback]
)

Epoch 1/25
259/259 [==============================] - 32s 122ms/step - loss: 0.6904 - accuracy: 0.5390 - val_loss: 0.6899 - val_accuracy: 0.5479
Epoch 2/25
259/259 [==============================] - 31s 119ms/step - loss: 0.6866 - accuracy: 0.5627 - val_loss: 0.6828 - val_accuracy: 0.5640
Epoch 3/25
259/259 [==============================] - 31s 118ms/step - loss: 0.6833 - accuracy: 0.5693 - val_loss: 0.6898 - val_accuracy: 0.5659
Epoch 4/25
259/259 [==============================] - 30s 117ms/step - loss: 0.6795 - accuracy: 0.5724 - val_loss: 0.6727 - val_accuracy: 0.5815
Epoch 5/25
259/259 [==============================] - 31s 118ms/step - loss: 0.6736 - accuracy: 0.5817 - val_loss: 0.6778 - val_accuracy: 0.5659
Epoch 6/25
259/259 [==============================] - 31s 119ms/step - loss: 0.6740 - accuracy: 0.5780 - val_loss: 0.6684 - val_accuracy: 0.6021
Epoch 7/25
259/259 [==============================] - 31s 119ms/step - loss: 0.6696 - accuracy: 0.5864 - val_loss: 0.6593 - val_accuracy: 0.6128
Epoch 8/25
259/259 [==============================] - 31s 119ms/step - loss: 0.6609 - accuracy: 0.6126 - val_loss: 0.6532 - val_accuracy: 0.6255
Epoch 9/25
259/259 [==============================] - 31s 121ms/step - loss: 0.6514 - accuracy: 0.6143 - val_loss: 0.6465 - val_accuracy: 0.6367
Epoch 10/25
259/259 [==============================] - 31s 118ms/step - loss: 0.6462 - accuracy: 0.6288 - val_loss: 0.6326 - val_accuracy: 0.6504
Epoch 11/25
259/259 [==============================] - 31s 120ms/step - loss: 0.6384 - accuracy: 0.6365 - val_loss: 0.6229 - val_accuracy: 0.6509
Epoch 12/25
259/259 [==============================] - 31s 120ms/step - loss: 0.6233 - accuracy: 0.6491 - val_loss: 0.6122 - val_accuracy: 0.6719
Epoch 13/25
259/259 [==============================] - 31s 121ms/step - loss: 0.6149 - accuracy: 0.6632 - val_loss: 0.5985 - val_accuracy: 0.6802
Epoch 14/25
259/259 [==============================] - 31s 121ms/step - loss: 0.6021 - accuracy: 0.6780 - val_loss: 0.5982 - val_accuracy: 0.6738
Epoch 15/25
259/259 [==============================] - 31s 121ms/step - loss: 0.5864 - accuracy: 0.6895 - val_loss: 0.5718 - val_accuracy: 0.7021
Epoch 16/25
259/259 [==============================] - 32s 124ms/step - loss: 0.5920 - accuracy: 0.6831 - val_loss: 0.5658 - val_accuracy: 0.7080
Epoch 17/25
259/259 [==============================] - 32s 122ms/step - loss: 0.5690 - accuracy: 0.7084 - val_loss: 0.5675 - val_accuracy: 0.7046
Epoch 18/25
259/259 [==============================] - 31s 121ms/step - loss: 0.5597 - accuracy: 0.7096 - val_loss: 0.5648 - val_accuracy: 0.7021
Epoch 19/25
259/259 [==============================] - 31s 121ms/step - loss: 0.5467 - accuracy: 0.7228 - val_loss: 0.5389 - val_accuracy: 0.7280
Epoch 20/25
259/259 [==============================] - 31s 120ms/step - loss: 0.5330 - accuracy: 0.7373 - val_loss: 0.5642 - val_accuracy: 0.7148
Epoch 21/25
259/259 [==============================] - 31s 120ms/step - loss: 0.5249 - accuracy: 0.7381 - val_loss: 0.5100 - val_accuracy: 0.7427
Epoch 22/25
259/259 [==============================] - 31s 121ms/step - loss: 0.5123 - accuracy: 0.7474 - val_loss: 0.5271 - val_accuracy: 0.7285
Epoch 23/25
259/259 [==============================] - 32s 122ms/step - loss: 0.5038 - accuracy: 0.7552 - val_loss: 0.4954 - val_accuracy: 0.7544
Epoch 24/25
259/259 [==============================] - 31s 120ms/step - loss: 0.5033 - accuracy: 0.7537 - val_loss: 0.4927 - val_accuracy: 0.7588
Epoch 25/25
259/259 [==============================] - 31s 120ms/step - loss: 0.4878 - accuracy: 0.7620 - val_loss: 0.4820 - val_accuracy: 0.7598

# lets visualize our new training_logs.csv file to find max accuracy and loss values
log_path = '/content/Adamtraining_logs.csv'
df = pd.read_csv(log_path, delimiter=';')
df 

	epoch	accuracy	loss	val_accuracy	val_loss
0	0	0.539020	0.690437	0.547852	0.689944
1	1	0.562734	0.686640	0.563965	0.682761
2	2	0.569268	0.683306	0.565918	0.689816
3	3	0.572414	0.679533	0.581543	0.672747
4	4	0.581730	0.673555	0.565918	0.677783
5	5	0.577979	0.673982	0.602051	0.668389
6	6	0.586449	0.669636	0.612793	0.659278
7	7	0.612583	0.660856	0.625488	0.653203
8	8	0.614277	0.651432	0.636719	0.646522
9	9	0.628796	0.646218	0.650391	0.632567
10	10	0.636540	0.638411	0.650879	0.622854
11	11	0.649123	0.623278	0.671875	0.612239
12	12	0.663158	0.614859	0.680176	0.598472
13	13	0.678040	0.602126	0.673828	0.598157
14	14	0.689534	0.586369	0.702148	0.571798
15	15	0.683122	0.591968	0.708008	0.565826
16	16	0.708409	0.569003	0.704590	0.567496
17	17	0.709619	0.559664	0.702148	0.564764
18	18	0.722807	0.546677	0.728027	0.538946
19	19	0.737326	0.533014	0.714844	0.564228
20	20	0.738052	0.524917	0.742676	0.510024
21	21	0.747368	0.512303	0.728516	0.527073
22	22	0.755233	0.503846	0.754395	0.495352
23	23	0.753660	0.503300	0.758789	0.492701
24	24	0.762008	0.487846	0.759766	0.481956

For epoch 25 (24 in the dataframe) we can see that Adam optimizer gives us a train accuracy of 0.762, a train loss of 0.487, a validation accuracy of 0.759, and a validation loss of 0.481. The time is took for it to complete was -> 31s 120ms/step.

CNN Adam Optimizer Highest Scores from DataFrame

max_acc = df['accuracy'].max()
max_loss = df['loss'].max()

train_acc = round(max_acc, 3)
train_loss = round(max_loss, 3)

print('The highest train loss of CNN Model (Adam optimizer) is:', train_loss)
print('The highest train accuracy of CNN Model (Adam optimizer) is:', train_acc)

The highest train loss of CNN Model (Adam optimizer) is: 0.69
The highest train accuracy of CNN Model (Adam optimizer) is: 0.762

max_val_acc = df['val_accuracy'].max()
max_val_loss = df['val_loss'].max()


val_acc = round(max_val_acc, 3)
val_loss = round(max_val_loss, 3)

print('The highest validation loss of CNN Model (Adam optimizer) is:', val_loss)
print('The highest validation accuracy of CNN Model (Adam optimizer) is:', val_acc)

The highest validation loss of CNN Model (Adam optimizer) is: 0.69
The highest validation accuracy of CNN Model (Adam optimizer) is: 0.76

4.5.4.3 Test

For the testing, we used 2 different approaches: model.evaluate_generator (using the sources provided) and the tutorial way slightly modified.

# https://stackoverflow.com/questions/63684459/should-i-use-evaluate-generator-or-evaluate-to-evaluate-my-cnn-model
# https://www.tensorflow.org/guide/keras/train_and_evaluate

# evaluate_generator is part of keras module in TensorFlow
test_loss, test_accuracy = model.evaluate_generator(test_generator, steps=len(X_test) // batch) 

tst_acc = round(test_accuracy,3)
tst_loss = round(test_loss,3)

print('The test loss for CNN Model is:', tst_loss)
print('The test accuracy for CNN Model is:', tst_acc)

The test loss for CNN Model is: 0.487
The test accuracy for CNN Model is: 0.76

Just like Training and Validation, our Test follows the same format as the tutorial with some few changes to fit our data and to print out only 6 cat or dog images. As Sklearn, we are using model,predict(X_test) to test our model with a new unknown set of images. Th predicted probabilities from model.predict() will be stored in a csv file containing image name and probabilities side by side in the entire X_test dataset. However, in our case, we just want 6 images for visualization purposes so the test.csv file will only contain 6 images along their respective probabilities.

TEST_SIZE = len(X_test)
TEST_FILE = 'Adamtest_results.csv'

probabilities = model.predict(X_test)

with open(TEST_FILE, 'w', newline='') as csvfile:
    fieldnames = ['filename', 'probability']
    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

    writer.writeheader()
    
    # randomly select 6 images
    indices = random.sample(range(TEST_SIZE), 6)

    fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(12, 8))
    ax = axes.ravel()

    for i, index in enumerate(indices):
        filename = f"image{index}.jpg"
        probability = probabilities[index]
        if probability[0] > 0.5: 
            result = "%.2f" % (probability[0]*100) + "% dog"
        else:
            result = "%.2f" % ((1-probability[0])*100) + "% cat"

        writer.writerow({'filename': filename, 'probability': result})

        # plot the image with predicted probability
        ax[i].imshow(X_test[index])
        ax[i].set_title(result)
        ax[i].axis('off')
        
    plt.tight_layout()
    plt.show()

82/82 [==============================] - 1s 14ms/step

test_path = '/content/Adamtest_results.csv'
df = pd.read_csv(test_path)
df 

	filename	probability
0	image1905.jpg	100.00% cat
1	image2151.jpg	100.00% cat
2	image1408.jpg	100.00% cat
3	image1113.jpg	100.00% cat
4	image309.jpg	100.00% cat
5	image1963.jpg	100.00% dog

Changing optimizer matters, RMSprop gave us worse predictions compared to Adam. A we all know; Adam has been the to go optimizer in this whole project. Although Adam gave better results in compared to RMS, our CNN model still needs more tunning for it to work 100% correctly (if possible*). Even though some of the above images won’t match, our new CNN still predict images way better (while using Adam optimizer) than the PyTorch MLP classification model from Phase 3.

4.5.5 Classification Report and Confusion Matrix

y_pred = model.predict(X_test)
y_pred_classes = np.argmax(y_pred, axis=1)

target_names = ['cat', 'dog']
print(classification_report(np.argmax(y_test, axis=1), y_pred_classes, target_names=target_names))

82/82 [==============================] - 1s 11ms/step
              precision    recall  f1-score   support

         cat       0.82      0.29      0.43      1209
         dog       0.60      0.94      0.74      1385

    accuracy                           0.64      2594
   macro avg       0.71      0.62      0.58      2594
weighted avg       0.70      0.64      0.59      2594

If we look closer to the classification report, we can see that the metrics indicate that our model is having an easier time identifying dog images than cat images. We have the same scenario back in Phase 3 where the model was having a hard time trying to correctly classify cat images. However, if we compare the Keras CNN model vs the PyTorch MLP model, we can see a significant increase in performance. Just by looking at the classification, we see that the model is performing way better than the MLP model from Phase 3. It is a major improvement.

PyTorch MLP Model Classification Report

\begin{equation*} \begin{array}{c|cc} & \text{Predicted Positive} & \text{Predicted Negative} \\ \hline \text{Actual Positive} & TP & FN \\ \text{Actual Negative} & FP & TN \\ \end{array} \end{equation*}

y_pred_binary = np.argmax(y_pred, axis=1)

cm = confusion_matrix(y_pred_classes, y_pred_binary)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=target_names)
disp.plot()
plt.show()

Again, we can see that our CNN Model (using Adam Optimzer) is performing way better than PyTorch MLP. The confusion matrix shows us how the model is performing regarding classifying both classes. We know there's more tunning to do, but the fact that it shows 0 for both FN and FP it's a good sign. Major improvement since Phase 3.

Take a look at Phase 3 PyTorch MLP Model Confusion Matrix:

4.5.6 Experiments Adam Optimizer vs RMSprop Optimizer

4.5.6.1 Adam Optimizer (Highest Score)

You can search through Tensorboard for any graph or any other relevant information regarding CNN Model behavior using this optimizer.

adamdf = pd.read_csv('/content/Adamtraining_logs.csv', delimiter=';')

ADAM_ACC = round(adamdf['accuracy'].max(), 3)
ADAM_LOSS = round(adamdf['loss'].max(), 3)
ADAM_VAL_ACC = round(adamdf['val_accuracy'].max(), 3)
ADAM_VAL_LOSS = round(adamdf['val_loss'].max(), 3)

ADAM = pd.DataFrame({'Adam Train Accuracy': [ADAM_ACC], 'Adam Train loss': [ADAM_LOSS], 'Adam Validation Accuracy': [ADAM_VAL_ACC], 'Adam Validation Loss': [ADAM_VAL_LOSS]})

ADAM

	Adam Train Accuracy	Adam Train loss	Adam Validation Accuracy	Adam Validation Loss
0	0.762	0.69	0.76	0.69

Adam Training loss vs Validation loss TensorBoard

4.5.6.2 RMSprop Optimizer (Highest Score)

rmsdf = pd.read_csv('/content/RMStraining_logs.csv', delimiter=';')

RMS_ACC = round(rmsdf['accuracy'].max(), 3)
RMS_LOSS = round(rmsdf['loss'].max(), 3)
RMS_VAL_ACC = round(rmsdf['val_accuracy'].max(), 3)
RMS_VAL_LOSS = round(rmsdf['val_loss'].max(), 3)

RMS = pd.DataFrame({'RMSprop Train Accuracy': [RMS_ACC], 'RMS Train loss': [RMS_LOSS], 'RMS Validation Accuracy': [RMS_VAL_ACC], 'RMS Validation Loss': [RMS_VAL_LOSS]})

RMS

	RMSprop Train Accuracy	RMS Train loss	RMS Validation Accuracy	RMS Validation Loss
0	0.705	0.691	0.713	0.692

Test Accuracy and Validation Loss for RMSprop Optimizer

RMSprop Training loss vs Validation loss TensorBoard

As we have mentioned before, the scores and graphs just show that Adam Optimizer has always been the optimizer that gives better results overall at least from a personal point of view. Adam returns 0.762 for train accuracy and 0.76 for validation accuracy. Meanwhile, RMSprop returns 0.705 for train accuracy and 0.713 for validation accuracy. Also, Adam optimizer returns the highest test accuracy of 0.76 vs RMSprop test accuracy of 0.695.

4.5.7 Observations

We tried to come up with the idea of using the entire Kaggle dataset for the Cat and Dog competition to generate even better results. After numerous attempts we finally managed to load the entire Kaggle dataset to our Google Drive and then proceeded to unzip the files. After that we created some subdirectories for cat and dog classes in train, validation, and test1 files to work with the entire data using ‘Keras .flow_from_directory()’ (required for this ImageDataGenerator step). However, we were unsuccess to even train the model due to the amount of resources the training needed per epoch. From the 100% computing units Google Colab Pro provides, almost 30% was consumed within the first 25 minutes of the first epoch training. We ended up dropping the idea and continue using the Sklearn method provided from HWs.

4.6 FCN

Fully Convolutional Networks (FCN) are similar to convolutional neural networks (CNN) but FCN do not use dense layers which receive input from all the neurons from the previous layer and instead are composed of convolutional layers from end-to-end. FCN used in this project is based on this tutorial and is composed of a total of 30 layers which are a combination of several convolutional 2d, dropout, normalization, and activation layers. The very last layer is a softmax function which is used for the object classification.

I did several experiments with changing batch sizes, different epochs, optimizers (Adam and SGD), and different dropout rates in the model. Based on the previous experiments, below are represented examples of Adam with batch size of 25 and Adam and SGD with batch size 50 which yielded the best results. For the batch size of 50, I needed to use Google Colab runtime with High-RAM and GPU.

Resources:

• https://towardsdatascience.com/implementing-a-fully-convolutional-network-fcn-in-tensorflow-2-3c46fb61de3b
• https://towardsdatascience.com/introduction-to-convolutional-neural-network-cnn-de73f69c5b83
• https://gsurma.medium.com/image-classifier-cats-vs-dogs-with-convolutional-neural-networks-cnns-and-google-colabs-4e9af21ae7a8

Model representation from TensorBoard.

4.6.1 Preparation

This section loads the data and prepare it for for the FCN model.

4.6.1.1 Loading Dependencies

This section loads all necessary Python libraries and connects the Colab environment to the data located in a shared Google Drive directory.

import pandas as pd
import numpy as np
import csv
import random
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay

from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.utils import plot_model
from keras.callbacks import CSVLogger

from google.colab import auth
from google.auth import default
from google.colab import drive
'''
Mounting Google Drive to the current Colab session
How to Connect Google Colab with Google Drive:
https://www.marktechpost.com/2019/06/07/how-to-connect-google-colab-with-google-drive/
'''
drive.mount('/content/drive', force_remount=True)

Mounted at /content/drive

4.6.1.2 Data Preprocessing

Loading the dataset from the previous project Phases. Each image is represented as 128x128x3 NumPy array.

colab_path = '/content/drive/MyDrive/aml/aml/data/martin'

X = np.load(colab_path + '/img.npy', allow_pickle=True)
y_label = np.load(colab_path + '/y_label.npy', allow_pickle=True)
y_bbox = np.load(colab_path + '/y_bbox.npy', allow_pickle=True)

Splitting the dataset on 80% for training and 20% for testing. And then further splitting the training dataset to 80% for training and 20% for validation.

# Testing
X_train, X_test, y_train, y_test_label = train_test_split(X, y_label, test_size=0.2, random_state=42)

# Trainging and Validation
X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.2, random_state=42)

print('Training size:', X_train.shape)
print('Validation size:', X_valid.shape)
print('Testing size:', X_test.shape)

Training size: (8297, 49152)
Validation size: (2075, 49152)
Testing size: (2594, 49152)

Reshaping datasets for further augmentation.

X_train = X_train.reshape((-1, 128, 128, 3))
X_valid = X_valid.reshape((-1, 128, 128, 3))
X_test = X_test.reshape((-1, 128, 128, 3))

num_classes = len(set(y_label))
y_train = to_categorical(y_train, num_classes)
y_valid = to_categorical(y_valid, num_classes)
y_test = to_categorical(y_test_label, num_classes)

4.6.1.3 Data Augmentation

This step augments the images in various ways (rotation, shifting width, shifting height, zoom, horizontal flip, etc.). The purpose is to provide larger input data variety to the model.

#https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator

train_generator = ImageDataGenerator(
    rescale=1./255,
    rotation_range=30,
    width_shift_range=0.1,
    height_shift_range=0.1,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest')

# Not augmenting validation and testing images
valid_generator = ImageDataGenerator(rescale=1./255)
test_generator = ImageDataGenerator(rescale=1./255)

train_generator.fit(X_train)
valid_generator.fit(X_valid)
test_generator.fit(X_test)

Creating the generators with batch sizes of 25.

batch = 25

train_generator = train_generator.flow(X_train, y_train, batch_size=batch)
valid_generator = valid_generator.flow(X_valid, y_valid, batch_size=batch)
test_generator = test_generator.flow(X_test, y_test, batch_size=batch)

4.6.2 Launching TensorBoard

TensorBoard is one of several ways we can visualize a model's progres either in real time or after the fact. TensorBoard in this project represents both training and validation in the real time.

Since each training task initializes a different TensorBoard callback, multiple training and validation graphs can be visualized in one instance of TensorBoard.

# Clear any logs from previous runs
!rm -rf logs/

import tensorflow as tf
import datetime

!pip install -q -U tensorboard
%load_ext tensorboard

log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
%tensorboard --logdir logs/fit

4.6.3 FCN Model 1 (Adam, small batch size)

FCN model is based on this tutorial.

import tensorflow as tf

def FCN_model(len_classes=2, dropout_rate=0.2):
    
    input = tf.keras.layers.Input(shape=(None, None, 3))

    x = tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=1)(input)
    x = tf.keras.layers.Dropout(dropout_rate)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)

    # x = tf.keras.layers.MaxPooling2D()(x)

    x = tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=1)(x)
    x = tf.keras.layers.Dropout(dropout_rate)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)

    # x = tf.keras.layers.MaxPooling2D()(x)

    x = tf.keras.layers.Conv2D(filters=128, kernel_size=3, strides=2)(x)
    x = tf.keras.layers.Dropout(dropout_rate)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)

    # x = tf.keras.layers.MaxPooling2D()(x)

    x = tf.keras.layers.Conv2D(filters=256, kernel_size=3, strides=2)(x)
    x = tf.keras.layers.Dropout(dropout_rate)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)

    # x = tf.keras.layers.MaxPooling2D()(x)

    x = tf.keras.layers.Conv2D(filters=512, kernel_size=3, strides=2)(x)
    x = tf.keras.layers.Dropout(dropout_rate)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)

    # Uncomment the below line if you're using dense layers
    # x = tf.keras.layers.GlobalMaxPooling2D()(x)

    # Fully connected layer 1
    # x = tf.keras.layers.Dropout(dropout_rate)(x)
    # x = tf.keras.layers.BatchNormalization()(x)
    # x = tf.keras.layers.Dense(units=64)(x)
    # x = tf.keras.layers.Activation('relu')(x)

    # Fully connected layer 1
    x = tf.keras.layers.Conv2D(filters=64, kernel_size=1, strides=1)(x)
    x = tf.keras.layers.Dropout(dropout_rate)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)

    # Fully connected layer 2
    # x = tf.keras.layers.Dropout(dropout_rate)(x)
    # x = tf.keras.layers.BatchNormalization()(x)
    # x = tf.keras.layers.Dense(units=len_classes)(x)
    # predictions = tf.keras.layers.Activation('softmax')(x)

    # Fully connected layer 2
    x = tf.keras.layers.Conv2D(filters=len_classes, kernel_size=1, strides=1)(x)
    x = tf.keras.layers.Dropout(dropout_rate)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.GlobalMaxPooling2D()(x)
    predictions = tf.keras.layers.Activation('softmax')(x)

    model = tf.keras.Model(inputs=input, outputs=predictions)

    return model

model = FCN_model(len_classes=2, dropout_rate=0.2)

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
            loss='binary_crossentropy',
            metrics=['accuracy'])

print(model.summary())
print(f'Total number of layers: {len(model.layers)}')

Model: "model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_1 (InputLayer)        [(None, None, None, 3)]   0         
                                                                 
 conv2d (Conv2D)             (None, None, None, 32)    896       
                                                                 
 dropout (Dropout)           (None, None, None, 32)    0         
                                                                 
 batch_normalization (BatchN  (None, None, None, 32)   128       
 ormalization)                                                   
                                                                 
 activation (Activation)     (None, None, None, 32)    0         
                                                                 
 conv2d_1 (Conv2D)           (None, None, None, 64)    18496     
                                                                 
 dropout_1 (Dropout)         (None, None, None, 64)    0         
                                                                 
 batch_normalization_1 (Batc  (None, None, None, 64)   256       
 hNormalization)                                                 
                                                                 
 activation_1 (Activation)   (None, None, None, 64)    0         
                                                                 
 conv2d_2 (Conv2D)           (None, None, None, 128)   73856     
                                                                 
 dropout_2 (Dropout)         (None, None, None, 128)   0         
                                                                 
 batch_normalization_2 (Batc  (None, None, None, 128)  512       
 hNormalization)                                                 
                                                                 
 activation_2 (Activation)   (None, None, None, 128)   0         
                                                                 
 conv2d_3 (Conv2D)           (None, None, None, 256)   295168    
                                                                 
 dropout_3 (Dropout)         (None, None, None, 256)   0         
                                                                 
 batch_normalization_3 (Batc  (None, None, None, 256)  1024      
 hNormalization)                                                 
                                                                 
 activation_3 (Activation)   (None, None, None, 256)   0         
                                                                 
 conv2d_4 (Conv2D)           (None, None, None, 512)   1180160   
                                                                 
 dropout_4 (Dropout)         (None, None, None, 512)   0         
                                                                 
 batch_normalization_4 (Batc  (None, None, None, 512)  2048      
 hNormalization)                                                 
                                                                 
 activation_4 (Activation)   (None, None, None, 512)   0         
                                                                 
 conv2d_5 (Conv2D)           (None, None, None, 64)    32832     
                                                                 
 dropout_5 (Dropout)         (None, None, None, 64)    0         
                                                                 
 batch_normalization_5 (Batc  (None, None, None, 64)   256       
 hNormalization)                                                 
                                                                 
 activation_5 (Activation)   (None, None, None, 64)    0         
                                                                 
 conv2d_6 (Conv2D)           (None, None, None, 2)     130       
                                                                 
 dropout_6 (Dropout)         (None, None, None, 2)     0         
                                                                 
 batch_normalization_6 (Batc  (None, None, None, 2)    8         
 hNormalization)                                                 
                                                                 
 global_max_pooling2d (Globa  (None, 2)                0         
 lMaxPooling2D)                                                  
                                                                 
 activation_6 (Activation)   (None, 2)                 0         
                                                                 
=================================================================
Total params: 1,605,770
Trainable params: 1,603,654
Non-trainable params: 2,116
_________________________________________________________________
None
Total number of layers: 30

# https://machinelearningmastery.com/visualize-deep-learning-neural-network-model-keras/
plot_model(model, to_file='model_Adam.png', show_shapes=True)

4.6.3.1 Training

epochs = 50

csv_log = 'training_adam_25_logs.csv'

history = model.fit_generator(
    train_generator,
    steps_per_epoch=len(X_train) // batch,
    epochs=epochs,
    validation_data=valid_generator,
    validation_steps=len(X_valid) // batch,
    callbacks=[CSVLogger(csv_log, append=False, separator=";"), tensorboard_callback]
)

<ipython-input-17-6a008910a0d7>:5: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  history = model.fit_generator(

Epoch 1/50
331/331 [==============================] - 56s 116ms/step - loss: 0.7610 - accuracy: 0.5005 - val_loss: 0.7101 - val_accuracy: 0.4988
Epoch 2/50
331/331 [==============================] - 37s 112ms/step - loss: 0.7120 - accuracy: 0.5203 - val_loss: 0.7035 - val_accuracy: 0.5330
Epoch 3/50
331/331 [==============================] - 38s 113ms/step - loss: 0.7046 - accuracy: 0.5384 - val_loss: 0.6996 - val_accuracy: 0.5316
Epoch 4/50
331/331 [==============================] - 38s 115ms/step - loss: 0.7007 - accuracy: 0.5274 - val_loss: 0.6973 - val_accuracy: 0.5316
Epoch 5/50
331/331 [==============================] - 38s 115ms/step - loss: 0.6980 - accuracy: 0.5255 - val_loss: 0.6960 - val_accuracy: 0.5316
Epoch 6/50
331/331 [==============================] - 38s 115ms/step - loss: 0.6964 - accuracy: 0.5273 - val_loss: 0.6949 - val_accuracy: 0.5316
Epoch 7/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6950 - accuracy: 0.5261 - val_loss: 0.6940 - val_accuracy: 0.5316
Epoch 8/50
331/331 [==============================] - 39s 117ms/step - loss: 0.6942 - accuracy: 0.5255 - val_loss: 0.6934 - val_accuracy: 0.5316
Epoch 9/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6933 - accuracy: 0.5266 - val_loss: 0.6929 - val_accuracy: 0.5316
Epoch 10/50
331/331 [==============================] - 39s 116ms/step - loss: 0.6924 - accuracy: 0.5261 - val_loss: 0.6926 - val_accuracy: 0.5316
Epoch 11/50
331/331 [==============================] - 39s 118ms/step - loss: 0.6915 - accuracy: 0.5267 - val_loss: 0.6920 - val_accuracy: 0.5316
Epoch 12/50
331/331 [==============================] - 39s 117ms/step - loss: 0.6912 - accuracy: 0.5268 - val_loss: 0.6915 - val_accuracy: 0.5316
Epoch 13/50
331/331 [==============================] - 39s 117ms/step - loss: 0.6907 - accuracy: 0.5265 - val_loss: 0.6911 - val_accuracy: 0.5316
Epoch 14/50
331/331 [==============================] - 39s 117ms/step - loss: 0.6900 - accuracy: 0.5317 - val_loss: 0.6909 - val_accuracy: 0.5316
Epoch 15/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6892 - accuracy: 0.5366 - val_loss: 0.6902 - val_accuracy: 0.5316
Epoch 16/50
331/331 [==============================] - 39s 117ms/step - loss: 0.6890 - accuracy: 0.5389 - val_loss: 0.6900 - val_accuracy: 0.5316
Epoch 17/50
331/331 [==============================] - 39s 117ms/step - loss: 0.6885 - accuracy: 0.5465 - val_loss: 0.6893 - val_accuracy: 0.5398
Epoch 18/50
331/331 [==============================] - 39s 117ms/step - loss: 0.6881 - accuracy: 0.5430 - val_loss: 0.6897 - val_accuracy: 0.5292
Epoch 19/50
331/331 [==============================] - 39s 117ms/step - loss: 0.6879 - accuracy: 0.5505 - val_loss: 0.6901 - val_accuracy: 0.5316
Epoch 20/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6876 - accuracy: 0.5457 - val_loss: 0.6882 - val_accuracy: 0.5316
Epoch 21/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6872 - accuracy: 0.5571 - val_loss: 0.6892 - val_accuracy: 0.5316
Epoch 22/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6866 - accuracy: 0.5580 - val_loss: 0.6887 - val_accuracy: 0.5311
Epoch 23/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6859 - accuracy: 0.5539 - val_loss: 0.6882 - val_accuracy: 0.5316
Epoch 24/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6849 - accuracy: 0.5586 - val_loss: 0.6861 - val_accuracy: 0.5388
Epoch 25/50
331/331 [==============================] - 39s 117ms/step - loss: 0.6847 - accuracy: 0.5699 - val_loss: 0.6846 - val_accuracy: 0.5561
Epoch 26/50
331/331 [==============================] - 38s 115ms/step - loss: 0.6846 - accuracy: 0.5602 - val_loss: 0.6830 - val_accuracy: 0.5455
Epoch 27/50
331/331 [==============================] - 38s 115ms/step - loss: 0.6828 - accuracy: 0.5666 - val_loss: 0.6855 - val_accuracy: 0.5354
Epoch 28/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6822 - accuracy: 0.5588 - val_loss: 0.6819 - val_accuracy: 0.5320
Epoch 29/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6823 - accuracy: 0.5691 - val_loss: 0.6836 - val_accuracy: 0.5340
Epoch 30/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6813 - accuracy: 0.5653 - val_loss: 0.6827 - val_accuracy: 0.5398
Epoch 31/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6824 - accuracy: 0.5746 - val_loss: 0.6821 - val_accuracy: 0.5316
Epoch 32/50
331/331 [==============================] - 39s 118ms/step - loss: 0.6805 - accuracy: 0.5771 - val_loss: 0.6821 - val_accuracy: 0.5610
Epoch 33/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6808 - accuracy: 0.5740 - val_loss: 0.6805 - val_accuracy: 0.5528
Epoch 34/50
331/331 [==============================] - 39s 116ms/step - loss: 0.6790 - accuracy: 0.5783 - val_loss: 0.6787 - val_accuracy: 0.5576
Epoch 35/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6783 - accuracy: 0.5833 - val_loss: 0.6813 - val_accuracy: 0.5311
Epoch 36/50
331/331 [==============================] - 38s 116ms/step - loss: 0.6772 - accuracy: 0.5834 - val_loss: 0.6847 - val_accuracy: 0.5335
Epoch 37/50
331/331 [==============================] - 38s 115ms/step - loss: 0.6776 - accuracy: 0.5893 - val_loss: 0.6791 - val_accuracy: 0.5523
Epoch 38/50
331/331 [==============================] - 38s 115ms/step - loss: 0.6780 - accuracy: 0.5843 - val_loss: 0.6789 - val_accuracy: 0.5614
Epoch 39/50
331/331 [==============================] - 39s 118ms/step - loss: 0.6756 - accuracy: 0.5914 - val_loss: 0.6784 - val_accuracy: 0.5325
Epoch 40/50
331/331 [==============================] - 38s 115ms/step - loss: 0.6767 - accuracy: 0.5909 - val_loss: 0.6815 - val_accuracy: 0.5441
Epoch 41/50
331/331 [==============================] - 38s 115ms/step - loss: 0.6749 - accuracy: 0.6022 - val_loss: 0.6774 - val_accuracy: 0.5508
Epoch 42/50
331/331 [==============================] - 38s 115ms/step - loss: 0.6745 - accuracy: 0.5961 - val_loss: 0.6772 - val_accuracy: 0.5605
Epoch 43/50
331/331 [==============================] - 38s 115ms/step - loss: 0.6744 - accuracy: 0.6022 - val_loss: 0.6762 - val_accuracy: 0.5769
Epoch 44/50
331/331 [==============================] - 38s 115ms/step - loss: 0.6743 - accuracy: 0.6063 - val_loss: 0.6762 - val_accuracy: 0.6164
Epoch 45/50
331/331 [==============================] - 43s 128ms/step - loss: 0.6716 - accuracy: 0.6133 - val_loss: 0.6742 - val_accuracy: 0.5942
Epoch 46/50
331/331 [==============================] - 39s 117ms/step - loss: 0.6731 - accuracy: 0.6134 - val_loss: 0.6788 - val_accuracy: 0.5677
Epoch 47/50
331/331 [==============================] - 43s 130ms/step - loss: 0.6711 - accuracy: 0.6221 - val_loss: 0.6810 - val_accuracy: 0.5571
Epoch 48/50
331/331 [==============================] - 39s 118ms/step - loss: 0.6723 - accuracy: 0.6106 - val_loss: 0.6737 - val_accuracy: 0.6173
Epoch 49/50
331/331 [==============================] - 39s 118ms/step - loss: 0.6706 - accuracy: 0.6181 - val_loss: 0.6746 - val_accuracy: 0.6275
Epoch 50/50
331/331 [==============================] - 39s 118ms/step - loss: 0.6709 - accuracy: 0.6203 - val_loss: 0.6748 - val_accuracy: 0.5894

4.6.3.2 Testing

# https://stackoverflow.com/questions/63684459/should-i-use-evaluate-generator-or-evaluate-to-evaluate-my-cnn-model
# https://www.tensorflow.org/guide/keras/train_and_evaluate

# evaluate_generator is part of keras module in TensorFlow
model1_test_loss, model1_test_accuracy = model.evaluate_generator(test_generator, steps=len(X_test) // batch) 

print()
print('Test accuracy (FCN, batch size 25, Adam optimizer) = ', round(model1_test_accuracy,3))
print('Test loss (FCN, batch size 25, Adam optimizer) = ', round(model1_test_loss,3))

<ipython-input-19-f0989dcf1f0e>:5: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.
  model1_test_loss, model1_test_accuracy = model.evaluate_generator(test_generator, steps=len(X_test) // batch)

Test accuracy (FCN, batch size 25, Adam optimizer) =  0.592
Test loss (FCN, batch size 25, Adam optimizer) =  0.676

4.6.3.3 Results

df = pd.read_csv(csv_log, delimiter=';')

# Printing out only the epoch with the highest accuracy
fcn01 = df[df['accuracy'] == df['accuracy'].max()]
display(fcn01)

	epoch	accuracy	loss	val_accuracy	val_loss
46	46	0.622099	0.671054	0.557108	0.680974

fcn01_numpy = fcn01.to_numpy()
model1_data = np.array([['FCN (Adam, batch 25)', fcn01_numpy[0][0],
                                  round(fcn01_numpy[0][1], 3), round(fcn01_numpy[0][3], 3), round(model1_test_accuracy, 3),
                                  round(fcn01_numpy[0][2], 3), round(fcn01_numpy[0][4], 3), round(model1_test_loss, 3)]])

fcn_results = pd.DataFrame(data = model1_data, columns = ['Model', 'Epoch', 'Train Accuracy', 'Valid Accuracy', 'Test Accuracy', 'Train Loss', 'Valid Loss', 'Test Loss'])
display(fcn_results)

	Model	Epoch	Train Accuracy	Valid Accuracy	Test Accuracy	Train Loss	Valid Loss	Test Loss
0	FCN (Adam, batch 25)	46.0	0.622	0.557	0.592	0.671	0.681	0.676

TEST_SIZE = len(X_test)
TEST_FILE = 'testing_adam_25_results.csv'

probabilities = model.predict(X_test)

with open(TEST_FILE, 'w', newline='') as csvfile:
    fieldnames = ['filename', 'probability']
    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

    writer.writeheader()
    
    # randomly select 6 images
    indices = random.sample(range(TEST_SIZE), 6)

    fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(12, 8))
    ax = axes.ravel()

    for i, index in enumerate(indices):
        filename = f"image{index}.jpg"
        probability = probabilities[index]
        if probability[0] > 0.5: 
            result = "%.2f" % (probability[0]*100) + "% dog"
        else:
            result = "%.2f" % ((1-probability[0])*100) + "% cat"

        writer.writerow({'filename': filename, 'probability': result})

        # plot the image with predicted probability
        ax[i].imshow(X_test[index])
        ax[i].set_title(result)
        ax[i].axis('off')
        
    plt.tight_layout()
    plt.show()

82/82 [==============================] - 2s 24ms/step

y_pred = model.predict(X_test)
y_pred_classes = np.argmax(y_pred, axis=1)

# Generate classification report
target_names = ['cat', 'dog']
print(classification_report(np.argmax(y_test, axis=1), y_pred_classes, target_names=target_names))

82/82 [==============================] - 3s 25ms/step
              precision    recall  f1-score   support

         cat       0.48      0.72      0.57      1209
         dog       0.56      0.31      0.40      1385

    accuracy                           0.50      2594
   macro avg       0.52      0.52      0.49      2594
weighted avg       0.52      0.50      0.48      2594

4.6.4 FCN Model 2 (SGD)

This model uses the same FCM model with learning rate 0.0001

4.6.4.1 Loading data

This is needed because the batch size is doubled. Steps are the same as described in the section 4.1.

colab_path = '/content/drive/MyDrive/aml/aml/data/martin'

X = np.load(colab_path + '/img.npy', allow_pickle=True)
y_label = np.load(colab_path + '/y_label.npy', allow_pickle=True)
y_bbox = np.load(colab_path + '/y_bbox.npy', allow_pickle=True)

# Testing
X_train, X_test, y_train, y_test_label = train_test_split(X, y_label, test_size=0.2, random_state=42)

# Trainging and Validation
X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.2, random_state=42)

X_train = X_train.reshape((-1, 128, 128, 3))
X_valid = X_valid.reshape((-1, 128, 128, 3))
X_test = X_test.reshape((-1, 128, 128, 3))

num_classes = len(set(y_label))
y_train = to_categorical(y_train, num_classes)
y_valid = to_categorical(y_valid, num_classes)
y_test = to_categorical(y_test_label, num_classes)

#https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator

train_generator = ImageDataGenerator(
    rescale=1./255,
    rotation_range=30,
    width_shift_range=0.1,
    height_shift_range=0.1,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest')

valid_generator = ImageDataGenerator(rescale=1./255)
test_generator = ImageDataGenerator(rescale=1./255)

train_generator.fit(X_train)
valid_generator.fit(X_valid)
test_generator.fit(X_test)

The batch size of 50 is the only difference compared to the section 4.1 Preparation.

batch = 50

train_generator = train_generator.flow(X_train, y_train, batch_size=batch)
valid_generator = valid_generator.flow(X_valid, y_valid, batch_size=batch)
test_generator = test_generator.flow(X_test, y_test, batch_size=batch)

model2 = FCN_model(len_classes=2, dropout_rate=0.2)

model2.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.0001),
            loss='binary_crossentropy',
            metrics=['accuracy'])

log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

4.6.4.2 Training

epochs = 50

csv_log = 'training_sgd_50_logs.csv'

history = model2.fit_generator(
    train_generator,
    steps_per_epoch=len(X_train) // batch,
    epochs=epochs,
    validation_data=valid_generator,
    validation_steps=len(X_valid) // batch,
    callbacks=[CSVLogger(csv_log, append=False, separator=";"), tensorboard_callback]
)

<ipython-input-45-66864dc74f05>:5: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  history = model2.fit_generator(

Epoch 1/50
165/165 [==============================] - 44s 234ms/step - loss: 1.6133 - accuracy: 0.4824 - val_loss: 0.7025 - val_accuracy: 0.5332
Epoch 2/50
165/165 [==============================] - 36s 215ms/step - loss: 1.5000 - accuracy: 0.4731 - val_loss: 0.7261 - val_accuracy: 0.5312
Epoch 3/50
165/165 [==============================] - 36s 217ms/step - loss: 1.4254 - accuracy: 0.4721 - val_loss: 0.7459 - val_accuracy: 0.5307
Epoch 4/50
165/165 [==============================] - 37s 221ms/step - loss: 1.3741 - accuracy: 0.4547 - val_loss: 0.7604 - val_accuracy: 0.5327
Epoch 5/50
165/165 [==============================] - 38s 228ms/step - loss: 1.3076 - accuracy: 0.4645 - val_loss: 0.7711 - val_accuracy: 0.5293
Epoch 6/50
165/165 [==============================] - 37s 221ms/step - loss: 1.2519 - accuracy: 0.4617 - val_loss: 0.7779 - val_accuracy: 0.5278
Epoch 7/50
165/165 [==============================] - 37s 222ms/step - loss: 1.1954 - accuracy: 0.4756 - val_loss: 0.7849 - val_accuracy: 0.5288
Epoch 8/50
165/165 [==============================] - 37s 223ms/step - loss: 1.1407 - accuracy: 0.4794 - val_loss: 0.7911 - val_accuracy: 0.5268
Epoch 9/50
165/165 [==============================] - 37s 223ms/step - loss: 1.1020 - accuracy: 0.4809 - val_loss: 0.7936 - val_accuracy: 0.5254
Epoch 10/50
165/165 [==============================] - 38s 230ms/step - loss: 1.0634 - accuracy: 0.4968 - val_loss: 0.7913 - val_accuracy: 0.5273
Epoch 11/50
165/165 [==============================] - 40s 239ms/step - loss: 1.0366 - accuracy: 0.4876 - val_loss: 0.7885 - val_accuracy: 0.5195
Epoch 12/50
165/165 [==============================] - 40s 242ms/step - loss: 1.0130 - accuracy: 0.4939 - val_loss: 0.7858 - val_accuracy: 0.5185
Epoch 13/50
165/165 [==============================] - 40s 241ms/step - loss: 0.9906 - accuracy: 0.4922 - val_loss: 0.7807 - val_accuracy: 0.5185
Epoch 14/50
165/165 [==============================] - 37s 225ms/step - loss: 0.9728 - accuracy: 0.4861 - val_loss: 0.7768 - val_accuracy: 0.5200
Epoch 15/50
165/165 [==============================] - 37s 223ms/step - loss: 0.9554 - accuracy: 0.4894 - val_loss: 0.7720 - val_accuracy: 0.5229
Epoch 16/50
165/165 [==============================] - 37s 224ms/step - loss: 0.9416 - accuracy: 0.4969 - val_loss: 0.7689 - val_accuracy: 0.5205
Epoch 17/50
165/165 [==============================] - 37s 224ms/step - loss: 0.9269 - accuracy: 0.4860 - val_loss: 0.7643 - val_accuracy: 0.5244
Epoch 18/50
165/165 [==============================] - 37s 226ms/step - loss: 0.9157 - accuracy: 0.4975 - val_loss: 0.7604 - val_accuracy: 0.5249
Epoch 19/50
165/165 [==============================] - 38s 229ms/step - loss: 0.9044 - accuracy: 0.5027 - val_loss: 0.7568 - val_accuracy: 0.5220
Epoch 20/50
165/165 [==============================] - 37s 223ms/step - loss: 0.8976 - accuracy: 0.4862 - val_loss: 0.7539 - val_accuracy: 0.5229
Epoch 21/50
165/165 [==============================] - 37s 223ms/step - loss: 0.8875 - accuracy: 0.4853 - val_loss: 0.7512 - val_accuracy: 0.5239
Epoch 22/50
165/165 [==============================] - 37s 223ms/step - loss: 0.8792 - accuracy: 0.4876 - val_loss: 0.7481 - val_accuracy: 0.5210
Epoch 23/50
165/165 [==============================] - 38s 229ms/step - loss: 0.8716 - accuracy: 0.4915 - val_loss: 0.7455 - val_accuracy: 0.5176
Epoch 24/50
165/165 [==============================] - 37s 225ms/step - loss: 0.8638 - accuracy: 0.4955 - val_loss: 0.7431 - val_accuracy: 0.5210
Epoch 25/50
165/165 [==============================] - 37s 225ms/step - loss: 0.8583 - accuracy: 0.4866 - val_loss: 0.7409 - val_accuracy: 0.5239
Epoch 26/50
165/165 [==============================] - 37s 224ms/step - loss: 0.8525 - accuracy: 0.4916 - val_loss: 0.7387 - val_accuracy: 0.5229
Epoch 27/50
165/165 [==============================] - 37s 225ms/step - loss: 0.8470 - accuracy: 0.4979 - val_loss: 0.7368 - val_accuracy: 0.5229
Epoch 28/50
165/165 [==============================] - 37s 224ms/step - loss: 0.8407 - accuracy: 0.5118 - val_loss: 0.7349 - val_accuracy: 0.5220
Epoch 29/50
165/165 [==============================] - 37s 225ms/step - loss: 0.8354 - accuracy: 0.5020 - val_loss: 0.7328 - val_accuracy: 0.5215
Epoch 30/50
165/165 [==============================] - 37s 225ms/step - loss: 0.8316 - accuracy: 0.4874 - val_loss: 0.7314 - val_accuracy: 0.5224
Epoch 31/50
165/165 [==============================] - 37s 225ms/step - loss: 0.8271 - accuracy: 0.4922 - val_loss: 0.7300 - val_accuracy: 0.5229
Epoch 32/50
165/165 [==============================] - 37s 226ms/step - loss: 0.8233 - accuracy: 0.4973 - val_loss: 0.7288 - val_accuracy: 0.5224
Epoch 33/50
165/165 [==============================] - 37s 225ms/step - loss: 0.8188 - accuracy: 0.5020 - val_loss: 0.7274 - val_accuracy: 0.5234
Epoch 34/50
165/165 [==============================] - 37s 225ms/step - loss: 0.8151 - accuracy: 0.5054 - val_loss: 0.7260 - val_accuracy: 0.5220
Epoch 35/50
165/165 [==============================] - 37s 224ms/step - loss: 0.8117 - accuracy: 0.5038 - val_loss: 0.7248 - val_accuracy: 0.5239
Epoch 36/50
165/165 [==============================] - 38s 232ms/step - loss: 0.8090 - accuracy: 0.4986 - val_loss: 0.7238 - val_accuracy: 0.5215
Epoch 37/50
165/165 [==============================] - 39s 235ms/step - loss: 0.8061 - accuracy: 0.4929 - val_loss: 0.7225 - val_accuracy: 0.5229
Epoch 38/50
165/165 [==============================] - 41s 248ms/step - loss: 0.8028 - accuracy: 0.4895 - val_loss: 0.7215 - val_accuracy: 0.5268
Epoch 39/50
165/165 [==============================] - 41s 245ms/step - loss: 0.7993 - accuracy: 0.4996 - val_loss: 0.7205 - val_accuracy: 0.5259
Epoch 40/50
165/165 [==============================] - 39s 238ms/step - loss: 0.7969 - accuracy: 0.4991 - val_loss: 0.7199 - val_accuracy: 0.5234
Epoch 41/50
165/165 [==============================] - 39s 237ms/step - loss: 0.7942 - accuracy: 0.5047 - val_loss: 0.7187 - val_accuracy: 0.5273
Epoch 42/50
165/165 [==============================] - 41s 246ms/step - loss: 0.7915 - accuracy: 0.5008 - val_loss: 0.7180 - val_accuracy: 0.5263
Epoch 43/50
165/165 [==============================] - 41s 246ms/step - loss: 0.7891 - accuracy: 0.5005 - val_loss: 0.7173 - val_accuracy: 0.5288
Epoch 44/50
165/165 [==============================] - 41s 248ms/step - loss: 0.7862 - accuracy: 0.5072 - val_loss: 0.7166 - val_accuracy: 0.5293
Epoch 45/50
165/165 [==============================] - 39s 233ms/step - loss: 0.7844 - accuracy: 0.5085 - val_loss: 0.7161 - val_accuracy: 0.5259
Epoch 46/50
165/165 [==============================] - 40s 240ms/step - loss: 0.7826 - accuracy: 0.5106 - val_loss: 0.7151 - val_accuracy: 0.5298
Epoch 47/50
165/165 [==============================] - 40s 243ms/step - loss: 0.7808 - accuracy: 0.5019 - val_loss: 0.7146 - val_accuracy: 0.5254
Epoch 48/50
165/165 [==============================] - 40s 240ms/step - loss: 0.7789 - accuracy: 0.4978 - val_loss: 0.7140 - val_accuracy: 0.5259
Epoch 49/50
165/165 [==============================] - 41s 246ms/step - loss: 0.7757 - accuracy: 0.5161 - val_loss: 0.7133 - val_accuracy: 0.5278
Epoch 50/50
165/165 [==============================] - 40s 244ms/step - loss: 0.7752 - accuracy: 0.5059 - val_loss: 0.7130 - val_accuracy: 0.5259

4.6.4.3 Testing

# https://stackoverflow.com/questions/63684459/should-i-use-evaluate-generator-or-evaluate-to-evaluate-my-cnn-model
# https://www.tensorflow.org/guide/keras/train_and_evaluate

# evaluate_generator is part of keras module in TensorFlow
model2_test_loss, model2_test_accuracy = model2.evaluate_generator(test_generator, steps=len(X_test) // batch) 

print()
print('Test accuracy (FCN, batch size 50, SGD optimizer) = ', round(model2_test_accuracy,3))
print('Test loss (FCN, batch size 50, SGD optimizer) = ', round(model2_test_loss,3))

<ipython-input-51-211b37750733>:5: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.
  model2_test_loss, model2_test_accuracy = model2.evaluate_generator(test_generator, steps=len(X_test) // batch)

Test accuracy (FCN, batch size 50, SGD optimizer) =  0.529
Test loss (FCN, batch size 50, SGD optimizer) =  0.712

4.6.4.4 Results

df = pd.read_csv(csv_log, delimiter=';')

# Printing out only the epoch with the highest accuracy
fcn02 = df[df['accuracy'] == df['accuracy'].max()]
display(fcn02)

	epoch	accuracy	loss	val_accuracy	val_loss
48	48	0.516066	0.77568	0.527805	0.713343

y_pred = model2.predict(X_test)
y_pred_classes = np.argmax(y_pred, axis=1)

# Generate classification report
target_names = ['cat', 'dog']
print(classification_report(np.argmax(y_test, axis=1), y_pred_classes, target_names=target_names))

82/82 [==============================] - 2s 24ms/step
              precision    recall  f1-score   support

         cat       0.47      0.99      0.63      1209
         dog       0.47      0.01      0.02      1385

    accuracy                           0.47      2594
   macro avg       0.47      0.50      0.33      2594
weighted avg       0.47      0.47      0.31      2594

fcn02_numpy = fcn02.to_numpy()
model2_data = ['FCN (SGD, batch 50)', fcn02_numpy[0][0],
                                  round(fcn02_numpy[0][1], 3), round(fcn02_numpy[0][3], 3), round(model2_test_accuracy, 3),
                                  round(fcn02_numpy[0][2], 3), round(fcn02_numpy[0][4], 3), round(model2_test_loss, 3)]

fcn_results.loc[1,:8] = model2_data
display(fcn_results)

<ipython-input-65-909256f61667>:2: FutureWarning: Slicing a positional slice with .loc is not supported, and will raise TypeError in a future version.  Use .loc with labels or .iloc with positions instead.
  fcn_results.loc[1,:8] = model2_data

	Model	Epoch	Train Accuracy	Valid Accuracy	Test Accuracy	Train Loss	Valid Loss	Test Loss
0	FCN (Adam, batch 25)	46	0.622	0.557	0.592	0.671	0.681	0.676
1	FCN (SGD, batch 50)	48	0.516	0.528	0.529	0.776	0.713	0.712

4.6.5 FCN Model 3 (Adam)

model3 = FCN_model(len_classes=2, dropout_rate=0.2)

model3.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
            loss='binary_crossentropy',
            metrics=['accuracy'])

log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

4.6.5.3 Training

epochs = 50

csv_log = 'training_adam_50_logs.csv'

history = model3.fit_generator(
    train_generator,
    steps_per_epoch=len(X_train) // batch,
    epochs=epochs,
    validation_data=valid_generator,
    validation_steps=len(X_valid) // batch,
    callbacks=[CSVLogger(csv_log, append=False, separator=";"), tensorboard_callback]
)

<ipython-input-60-a39c4326f29e>:5: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  history = model3.fit_generator(

Epoch 1/50
165/165 [==============================] - 45s 247ms/step - loss: 0.8169 - accuracy: 0.4924 - val_loss: 0.7076 - val_accuracy: 0.5317
Epoch 2/50
165/165 [==============================] - 39s 236ms/step - loss: 0.7296 - accuracy: 0.4955 - val_loss: 0.7058 - val_accuracy: 0.5190
Epoch 3/50
165/165 [==============================] - 41s 246ms/step - loss: 0.7167 - accuracy: 0.4921 - val_loss: 0.7034 - val_accuracy: 0.5317
Epoch 4/50
165/165 [==============================] - 39s 237ms/step - loss: 0.7103 - accuracy: 0.4907 - val_loss: 0.7015 - val_accuracy: 0.5317
Epoch 5/50
165/165 [==============================] - 40s 242ms/step - loss: 0.7060 - accuracy: 0.5039 - val_loss: 0.6998 - val_accuracy: 0.5307
Epoch 6/50
165/165 [==============================] - 40s 240ms/step - loss: 0.7033 - accuracy: 0.5216 - val_loss: 0.6984 - val_accuracy: 0.5327
Epoch 7/50
165/165 [==============================] - 38s 230ms/step - loss: 0.7008 - accuracy: 0.5373 - val_loss: 0.6972 - val_accuracy: 0.5312
Epoch 8/50
165/165 [==============================] - 41s 246ms/step - loss: 0.6993 - accuracy: 0.5357 - val_loss: 0.6963 - val_accuracy: 0.5332
Epoch 9/50
165/165 [==============================] - 38s 232ms/step - loss: 0.6977 - accuracy: 0.5289 - val_loss: 0.6955 - val_accuracy: 0.5312
Epoch 10/50
165/165 [==============================] - 40s 240ms/step - loss: 0.6967 - accuracy: 0.5267 - val_loss: 0.6949 - val_accuracy: 0.5302
Epoch 11/50
165/165 [==============================] - 40s 240ms/step - loss: 0.6957 - accuracy: 0.5256 - val_loss: 0.6944 - val_accuracy: 0.5298
Epoch 12/50
165/165 [==============================] - 39s 236ms/step - loss: 0.6945 - accuracy: 0.5270 - val_loss: 0.6939 - val_accuracy: 0.5327
Epoch 13/50
165/165 [==============================] - 39s 238ms/step - loss: 0.6941 - accuracy: 0.5275 - val_loss: 0.6933 - val_accuracy: 0.5332
Epoch 14/50
165/165 [==============================] - 39s 234ms/step - loss: 0.6934 - accuracy: 0.5260 - val_loss: 0.6931 - val_accuracy: 0.5317
Epoch 15/50
165/165 [==============================] - 38s 231ms/step - loss: 0.6927 - accuracy: 0.5269 - val_loss: 0.6929 - val_accuracy: 0.5312
Epoch 16/50
165/165 [==============================] - 37s 224ms/step - loss: 0.6919 - accuracy: 0.5277 - val_loss: 0.6924 - val_accuracy: 0.5332
Epoch 17/50
165/165 [==============================] - 42s 255ms/step - loss: 0.6916 - accuracy: 0.5279 - val_loss: 0.6922 - val_accuracy: 0.5312
Epoch 18/50
165/165 [==============================] - 40s 239ms/step - loss: 0.6913 - accuracy: 0.5278 - val_loss: 0.6921 - val_accuracy: 0.5293
Epoch 19/50
165/165 [==============================] - 39s 238ms/step - loss: 0.6904 - accuracy: 0.5318 - val_loss: 0.6915 - val_accuracy: 0.5332
Epoch 20/50
165/165 [==============================] - 38s 227ms/step - loss: 0.6903 - accuracy: 0.5311 - val_loss: 0.6918 - val_accuracy: 0.5312
Epoch 21/50
165/165 [==============================] - 37s 226ms/step - loss: 0.6895 - accuracy: 0.5478 - val_loss: 0.6912 - val_accuracy: 0.5346
Epoch 22/50
165/165 [==============================] - 37s 224ms/step - loss: 0.6896 - accuracy: 0.5484 - val_loss: 0.6919 - val_accuracy: 0.5395
Epoch 23/50
165/165 [==============================] - 37s 225ms/step - loss: 0.6894 - accuracy: 0.5448 - val_loss: 0.6915 - val_accuracy: 0.5302
Epoch 24/50
165/165 [==============================] - 38s 228ms/step - loss: 0.6889 - accuracy: 0.5433 - val_loss: 0.6906 - val_accuracy: 0.5312
Epoch 25/50
165/165 [==============================] - 38s 229ms/step - loss: 0.6889 - accuracy: 0.5493 - val_loss: 0.6901 - val_accuracy: 0.5449
Epoch 26/50
165/165 [==============================] - 43s 258ms/step - loss: 0.6878 - accuracy: 0.5568 - val_loss: 0.6903 - val_accuracy: 0.5415
Epoch 27/50
165/165 [==============================] - 38s 228ms/step - loss: 0.6879 - accuracy: 0.5495 - val_loss: 0.6905 - val_accuracy: 0.5341
Epoch 28/50
165/165 [==============================] - 39s 233ms/step - loss: 0.6870 - accuracy: 0.5589 - val_loss: 0.6903 - val_accuracy: 0.5322
Epoch 29/50
165/165 [==============================] - 37s 225ms/step - loss: 0.6869 - accuracy: 0.5510 - val_loss: 0.6892 - val_accuracy: 0.5449
Epoch 30/50
165/165 [==============================] - 37s 225ms/step - loss: 0.6862 - accuracy: 0.5607 - val_loss: 0.6875 - val_accuracy: 0.5488
Epoch 31/50
165/165 [==============================] - 38s 231ms/step - loss: 0.6859 - accuracy: 0.5571 - val_loss: 0.6891 - val_accuracy: 0.5341
Epoch 32/50
165/165 [==============================] - 37s 223ms/step - loss: 0.6860 - accuracy: 0.5517 - val_loss: 0.6897 - val_accuracy: 0.5332
Epoch 33/50
165/165 [==============================] - 37s 226ms/step - loss: 0.6853 - accuracy: 0.5613 - val_loss: 0.6894 - val_accuracy: 0.5468
Epoch 34/50
165/165 [==============================] - 37s 224ms/step - loss: 0.6832 - accuracy: 0.5680 - val_loss: 0.6852 - val_accuracy: 0.5454
Epoch 35/50
165/165 [==============================] - 37s 224ms/step - loss: 0.6833 - accuracy: 0.5668 - val_loss: 0.6882 - val_accuracy: 0.5454
Epoch 36/50
165/165 [==============================] - 37s 224ms/step - loss: 0.6826 - accuracy: 0.5708 - val_loss: 0.6854 - val_accuracy: 0.5332
Epoch 37/50
165/165 [==============================] - 37s 223ms/step - loss: 0.6826 - accuracy: 0.5664 - val_loss: 0.6862 - val_accuracy: 0.5454
Epoch 38/50
165/165 [==============================] - 37s 223ms/step - loss: 0.6823 - accuracy: 0.5677 - val_loss: 0.6829 - val_accuracy: 0.5478
Epoch 39/50
165/165 [==============================] - 37s 225ms/step - loss: 0.6815 - accuracy: 0.5754 - val_loss: 0.6826 - val_accuracy: 0.5390
Epoch 40/50
165/165 [==============================] - 37s 224ms/step - loss: 0.6812 - accuracy: 0.5692 - val_loss: 0.6843 - val_accuracy: 0.5473
Epoch 41/50
165/165 [==============================] - 37s 224ms/step - loss: 0.6812 - accuracy: 0.5805 - val_loss: 0.6811 - val_accuracy: 0.5566
Epoch 42/50
165/165 [==============================] - 37s 226ms/step - loss: 0.6802 - accuracy: 0.5745 - val_loss: 0.6828 - val_accuracy: 0.5566
Epoch 43/50
165/165 [==============================] - 37s 223ms/step - loss: 0.6798 - accuracy: 0.5797 - val_loss: 0.6812 - val_accuracy: 0.5585
Epoch 44/50
165/165 [==============================] - 37s 224ms/step - loss: 0.6803 - accuracy: 0.5754 - val_loss: 0.6804 - val_accuracy: 0.5459
Epoch 45/50
165/165 [==============================] - 37s 222ms/step - loss: 0.6784 - accuracy: 0.5828 - val_loss: 0.6826 - val_accuracy: 0.5532
Epoch 46/50
165/165 [==============================] - 37s 225ms/step - loss: 0.6795 - accuracy: 0.5869 - val_loss: 0.6837 - val_accuracy: 0.5766
Epoch 47/50
165/165 [==============================] - 37s 223ms/step - loss: 0.6790 - accuracy: 0.5869 - val_loss: 0.6778 - val_accuracy: 0.5600
Epoch 48/50
165/165 [==============================] - 37s 224ms/step - loss: 0.6771 - accuracy: 0.5860 - val_loss: 0.6798 - val_accuracy: 0.5366
Epoch 49/50
165/165 [==============================] - 37s 223ms/step - loss: 0.6767 - accuracy: 0.5863 - val_loss: 0.6768 - val_accuracy: 0.5683
Epoch 50/50
165/165 [==============================] - 37s 225ms/step - loss: 0.6762 - accuracy: 0.5814 - val_loss: 0.6775 - val_accuracy: 0.5878

4.6.5.2 Testing

# https://stackoverflow.com/questions/63684459/should-i-use-evaluate-generator-or-evaluate-to-evaluate-my-cnn-model
# https://www.tensorflow.org/guide/keras/train_and_evaluate

# evaluate_generator is part of keras module in TensorFlow
model3_test_loss, model3_test_accuracy = model3.evaluate_generator(test_generator, steps=len(X_test) // batch) 

print()
print('Test accuracy (FCN, batch size 50, Adam optimizer) = ', round(model3_test_accuracy,3))
print('Test loss (FCN, batch size 50, Adam optimizer) = ', round(model3_test_loss,3))

<ipython-input-61-c764f886d1b7>:5: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.
  model3_test_loss, model3_test_accuracy = model3.evaluate_generator(test_generator, steps=len(X_test) // batch)

Test accuracy (FCN, batch size 50, Adam optimizer) =  0.578
Test loss (FCN, batch size 50, Adam optimizer) =  0.681

4.6.5.3 Results

df = pd.read_csv(csv_log, delimiter=';')

# Printing out only the epoch with the highest accuracy
fcn03 = df[df['accuracy'] == df['accuracy'].max()]
display(fcn03)

	epoch	accuracy	loss	val_accuracy	val_loss
45	45	0.58688	0.679518	0.576585	0.68371
46	46	0.58688	0.678986	0.560000	0.67781

y_pred = model3.predict(X_test)
y_pred_classes = np.argmax(y_pred, axis=1)

# Generate classification report
target_names = ['cat', 'dog']
print(classification_report(np.argmax(y_test, axis=1), y_pred_classes, target_names=target_names))

82/82 [==============================] - 2s 24ms/step
              precision    recall  f1-score   support

         cat       0.49      0.75      0.59      1209
         dog       0.59      0.31      0.41      1385

    accuracy                           0.52      2594
   macro avg       0.54      0.53      0.50      2594
weighted avg       0.54      0.52      0.49      2594

fcn03_numpy = fcn03.to_numpy()
model3_data = ['FCN (Adam, batch 50)', fcn03_numpy[0][0],
                                  round(fcn03_numpy[0][1], 3), round(fcn03_numpy[0][3], 3), round(model1_test_accuracy, 3),
                                  round(fcn03_numpy[0][2], 3), round(fcn03_numpy[0][4], 3), round(model1_test_loss, 3)]

fcn_results.loc[2,:8] = model3_data
display(fcn_results)

<ipython-input-68-01d14541c68e>:6: FutureWarning: Slicing a positional slice with .loc is not supported, and will raise TypeError in a future version.  Use .loc with labels or .iloc with positions instead.
  fcn_results.loc[2,:8] = model3_data

	Model	Epoch	Train Accuracy	Valid Accuracy	Test Accuracy	Train Loss	Valid Loss	Test Loss
0	FCN (Adam, batch 25)	46	0.622	0.557	0.592	0.671	0.681	0.676
1	FCN (SGD, batch 50)	48	0.516	0.528	0.529	0.776	0.713	0.712
2	FCN (Adam, batch 50)	45.0	0.587	0.577	0.592	0.68	0.684	0.676

4.6.6 FCN Results

Several experiments outside of this notebook were done to establish optimal dropout rate, number of hidden layers, and focusing on a larger number of epochs. Adam optimizer was assumed to perform the best but stochastic gradient descent (SGD) was used for comparison. The two models with Adam optimizers performed better based on training and testing accuracies and lower overall loss functions.

Their testing accuracies and loss functions were exactly the same 0.592 and 0.676. The model with batch size of 25 had slightly higher training accuracy 0.622 (compared to the validation and testing accuracies) which suggests a slight overfitting but its validation and testing accuracies are the same as the other Adam model with larger batch size. Also when taken into account precision from the confusion matrix was slightly higher at recognizing cats and dogs, the last model with Adam optimizer and batch size of 50 performed the best from the three available ones.

display(fcn_results)

	Model	Epoch	Train Accuracy	Valid Accuracy	Test Accuracy	Train Loss	Valid Loss	Test Loss
0	FCN (Adam, batch 25)	46	0.622	0.557	0.592	0.671	0.681	0.676
1	FCN (SGD, batch 50)	48	0.516	0.528	0.529	0.776	0.713	0.712
2	FCN (Adam, batch 50)	45.0	0.587	0.577	0.592	0.68	0.684	0.676

Confusion matrix of the last model (FCN (Adam, batch 50)):

# Started another instance of TensorBoard becuase the previous one had some issues

log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
%tensorboard --logdir logs/fit --port 6007

4.7 AlexNet

4.7.1 Loading Train and test data sets

colab_path = '/content/drive/MyDrive/aml'
train = colab_path + '/' + 'training_set/'
test = colab_path + '/' + 'test_set/'

4.7.2 Reshaping

cates = ['dogs', 'cats']
def load_images_and_labels(data_path, cates): 
  X = []
  y = []
  i = 0
  for index, cate in enumerate(cates): 
    for img_name in os.listdir(data_path + cate):
      i = i +1
      print(i)
      img = cv2.imread(data_path + cate + '/' + img_name)
      if img is not None: 
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img_array = Image.fromarray(img, 'RGB')
        # resize image to 227x227 which is the required input size of the Alexnet model
        img_rs = img_array.resize((227,227))
        # convert the image to array
        img_rs = np.array(img_rs)
      
        X.append(img_rs)
        y.append(index)
  return X, y

X_train, y_train = load_images_and_labels(train, cates)

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8000
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8007

X_test, y_test = load_images_and_labels(test, cates)

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4.7.3 Preprocessing

Here we convert X_train, and X_test to an array of float values and normalize then y_train and y_test to categorical values

from keras.utils import to_categorical
def preprocess_data(X, y):
  # convert X from list to array
  X = np.array(X)
  
  # convert integer values of X into floats
  X = X.astype(np.float32)

  # normalization 
  X = X/255.0
  
  # one-hot encoding the labels 
  y = to_categorical(np.array(y))
    
  return X, y

(X_train, y_train) = preprocess_data(X_train, y_train)
(X_test, y_test) = preprocess_data(X_test, y_test)

4.7.4 Defining the AlexNet Model

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout
# metric
from keras.metrics import binary_crossentropy
# optimization method (Stochastic Gradient Descent (SGD))
from keras.optimizers import SGD, Adam
from tensorflow.keras.utils import plot_model

def Alexnet():
  # Initialize the model
  model = Sequential()

  # layer 1: convolutional layer + max-pooling layer ##see below shape size 
  model.add(Conv2D(filters = 96, kernel_size = (11,11), strides= 4, padding = 'valid', activation='relu', input_shape = (227,227,3)))
  model.add(MaxPooling2D(pool_size = (3,3), strides = 2))

  # layer 2: convolutional layer + max-pooling layer 
  model.add(Conv2D(filters = 256, kernel_size = (5,5), padding = 'same', activation = 'relu'))
  model.add(MaxPooling2D(pool_size = (3,3), strides = 2))

  # layers 3-5: three convolutional layers + 1 max-pooling layer
  model.add(Conv2D(filters = 384, kernel_size = (3,3), padding = 'same', activation = 'relu'))
  model.add(Conv2D(filters = 384, kernel_size = (3,3), padding = 'same', activation = 'relu'))
  model.add(Conv2D(filters = 256, kernel_size = (3,3), padding = 'same', activation = 'relu'))
  model.add(MaxPooling2D(pool_size = (3,3), strides = 2))

  # layers 6 - 8: two fully connected hidden layers and one fully connected output layer
  model.add(Flatten())
  model.add(Dense(4096, activation = 'relu'))
  model.add(Dropout(0.5))
  model.add(Dense(4096, activation = 'relu'))
  model.add(Dropout(0.5))
  model.add(Dense(2, activation = 'softmax'))

  # compile the model with a loss funciton, a metric and and optimization method
  #opt = Adam(lr = 0.01) #0.001 - gives high loss values and lower accuracy
  opt = SGD(lr = 0.1) #0.1 gives the lowest loss values
  model.compile(loss = binary_crossentropy,
                optimizer = opt,
                metrics = ['accuracy'])

  return model

Alexnet_model = Alexnet()
Alexnet_model.summary()
plot_model(Alexnet_model, to_file='AlexnetModel.png', show_shapes=True)

Model: "sequential_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d_5 (Conv2D)           (None, 55, 55, 96)        34944     
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 27, 27, 96)       0         
 2D)                                                             
                                                                 
 conv2d_6 (Conv2D)           (None, 27, 27, 256)       614656    
                                                                 
 max_pooling2d_4 (MaxPooling  (None, 13, 13, 256)      0         
 2D)                                                             
                                                                 
 conv2d_7 (Conv2D)           (None, 13, 13, 384)       885120    
                                                                 
 conv2d_8 (Conv2D)           (None, 13, 13, 384)       1327488   
                                                                 
 conv2d_9 (Conv2D)           (None, 13, 13, 256)       884992    
                                                                 
 max_pooling2d_5 (MaxPooling  (None, 6, 6, 256)        0         
 2D)                                                             
                                                                 
 flatten_1 (Flatten)         (None, 9216)              0         
                                                                 
 dense_3 (Dense)             (None, 4096)              37752832  
                                                                 
 dropout_2 (Dropout)         (None, 4096)              0         
                                                                 
 dense_4 (Dense)             (None, 4096)              16781312  
                                                                 
 dropout_3 (Dropout)         (None, 4096)              0         
                                                                 
 dense_5 (Dense)             (None, 2)                 8194      
                                                                 
=================================================================
Total params: 58,289,538
Trainable params: 58,289,538
Non-trainable params: 0
_________________________________________________________________

4.7.5 Data Augmentation

Below, we applied a rotation and image flip in order to prevent overfitting

from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint

def train_model(model, X_train, y_train, X_test, y_test, epochs, batch_size):
  # Data generator
  datagen = ImageDataGenerator(rotation_range = 5, width_shift_range = 0.1, height_shift_range = 0.1, horizontal_flip = True)
  # iteration on the training set
  it_train = datagen.flow(X_train, y_train, batch_size = batch_size)
  # path to save checkpoint 
  path_cp = os.getcwd() + '/' + 'weights_.hdf5'
  checkpoint_ = ModelCheckpoint(path_cp, monitor = 'loss', save_best_only = True, mode = 'auto')
  steps = X_train.shape[0]//batch_size
  # Fitting the model
  history = model.fit_generator(it_train, epochs = epochs, steps_per_epoch = steps, 
                                validation_data = (X_test, y_test), verbose = 1, 
                                callbacks = checkpoint_)
  # Evaluating the model
  _, acc = model.evaluate(X_test, y_test, verbose = 1)
  print('%.3f' % (acc * 100.0))
  
  return history, acc

4.7.6 Results

Below we can see results from 30 epochs of our best tested model using stochastic gradient descent (SGD) and a learning rate of 0.1. Other results from previous tests can be found in the results and discussion section

train_history, acc = train_model(Alexnet_model, X_train, y_train, X_test, y_test, epochs = 30, batch_size = 128)

Epoch 1/30
62/62 [==============================] - 98s 1s/step - loss: 0.6928 - accuracy: 0.5161 - val_loss: 0.6915 - val_accuracy: 0.5002
Epoch 2/30
62/62 [==============================] - 83s 1s/step - loss: 0.6900 - accuracy: 0.5347 - val_loss: 0.6895 - val_accuracy: 0.5146
Epoch 3/30
62/62 [==============================] - 84s 1s/step - loss: 0.6895 - accuracy: 0.5422 - val_loss: 0.6925 - val_accuracy: 0.4978
Epoch 4/30
62/62 [==============================] - 85s 1s/step - loss: 0.6862 - accuracy: 0.5577 - val_loss: 0.6846 - val_accuracy: 0.5185
Epoch 5/30
62/62 [==============================] - 81s 1s/step - loss: 0.6815 - accuracy: 0.5632 - val_loss: 0.7324 - val_accuracy: 0.5091
Epoch 6/30
62/62 [==============================] - 83s 1s/step - loss: 0.6735 - accuracy: 0.5819 - val_loss: 0.6718 - val_accuracy: 0.5561
Epoch 7/30
62/62 [==============================] - 83s 1s/step - loss: 0.6749 - accuracy: 0.5845 - val_loss: 0.6713 - val_accuracy: 0.5512
Epoch 8/30
62/62 [==============================] - 82s 1s/step - loss: 0.6492 - accuracy: 0.6219 - val_loss: 0.6283 - val_accuracy: 0.6634
Epoch 9/30
62/62 [==============================] - 81s 1s/step - loss: 0.6515 - accuracy: 0.6275 - val_loss: 0.6462 - val_accuracy: 0.6357
Epoch 10/30
62/62 [==============================] - 80s 1s/step - loss: 0.6322 - accuracy: 0.6481 - val_loss: 0.6056 - val_accuracy: 0.6822
Epoch 11/30
62/62 [==============================] - 81s 1s/step - loss: 0.6277 - accuracy: 0.6524 - val_loss: 0.6247 - val_accuracy: 0.6574
Epoch 12/30
62/62 [==============================] - 80s 1s/step - loss: 0.6331 - accuracy: 0.6398 - val_loss: 0.6241 - val_accuracy: 0.6950
Epoch 13/30
62/62 [==============================] - 80s 1s/step - loss: 0.6251 - accuracy: 0.6551 - val_loss: 0.5595 - val_accuracy: 0.7261
Epoch 14/30
62/62 [==============================] - 80s 1s/step - loss: 0.5929 - accuracy: 0.6853 - val_loss: 0.5607 - val_accuracy: 0.7197
Epoch 15/30
62/62 [==============================] - 81s 1s/step - loss: 0.5781 - accuracy: 0.6957 - val_loss: 0.5298 - val_accuracy: 0.7360
Epoch 16/30
62/62 [==============================] - 80s 1s/step - loss: 0.5559 - accuracy: 0.7090 - val_loss: 0.5341 - val_accuracy: 0.7439
Epoch 17/30
62/62 [==============================] - 81s 1s/step - loss: 0.5492 - accuracy: 0.7217 - val_loss: 0.5872 - val_accuracy: 0.6817
Epoch 18/30
62/62 [==============================] - 80s 1s/step - loss: 0.5658 - accuracy: 0.7047 - val_loss: 0.5811 - val_accuracy: 0.6965
Epoch 19/30
62/62 [==============================] - 80s 1s/step - loss: 0.5095 - accuracy: 0.7483 - val_loss: 0.4387 - val_accuracy: 0.7968
Epoch 20/30
62/62 [==============================] - 80s 1s/step - loss: 0.4845 - accuracy: 0.7643 - val_loss: 0.4550 - val_accuracy: 0.7924
Epoch 21/30
62/62 [==============================] - 80s 1s/step - loss: 0.4680 - accuracy: 0.7739 - val_loss: 0.5240 - val_accuracy: 0.7385
Epoch 22/30
62/62 [==============================] - 79s 1s/step - loss: 0.4615 - accuracy: 0.7825 - val_loss: 0.3917 - val_accuracy: 0.8230
Epoch 23/30
62/62 [==============================] - 79s 1s/step - loss: 0.4359 - accuracy: 0.7940 - val_loss: 0.4283 - val_accuracy: 0.8072
Epoch 24/30
62/62 [==============================] - 80s 1s/step - loss: 0.4107 - accuracy: 0.8113 - val_loss: 0.3789 - val_accuracy: 0.8398
Epoch 25/30
62/62 [==============================] - 79s 1s/step - loss: 0.4197 - accuracy: 0.8058 - val_loss: 0.3575 - val_accuracy: 0.8433
Epoch 26/30
62/62 [==============================] - 80s 1s/step - loss: 0.3993 - accuracy: 0.8160 - val_loss: 0.3631 - val_accuracy: 0.8379
Epoch 27/30
62/62 [==============================] - 80s 1s/step - loss: 0.3911 - accuracy: 0.8214 - val_loss: 0.3652 - val_accuracy: 0.8458
Epoch 28/30
62/62 [==============================] - 80s 1s/step - loss: 0.3750 - accuracy: 0.8323 - val_loss: 0.3269 - val_accuracy: 0.8606
Epoch 29/30
62/62 [==============================] - 80s 1s/step - loss: 0.3549 - accuracy: 0.8408 - val_loss: 0.3406 - val_accuracy: 0.8537
Epoch 30/30
62/62 [==============================] - 79s 1s/step - loss: 0.3866 - accuracy: 0.8157 - val_loss: 0.3413 - val_accuracy: 0.8473
64/64 [==============================] - 1s 19ms/step - loss: 0.3413 - accuracy: 0.8473
84.726

Saving results as CSV

Results_SGD1 = {'train_loss': train_history.history['loss'],
                'train_accuracy': train_history.history['accuracy'],
                'val_loss': train_history.history['val_loss'],
                'val_accuracy': train_history.history['val_accuracy']}
results_df = pd.DataFrame(Results_SGD1)
results_df.to_csv('ResultsAlexSGD1.csv', index=1)

'''
Results_SGD2 = {'train_loss': train_history.history['loss'],
                'train_accuracy': train_history.history['accuracy'],
                'val_loss': train_history.history['val_loss'],
                'val_accuracy': train_history.history['val_accuracy']}
results_df = pd.DataFrame(Results_SGD2)
results_df.to_csv('ResultsAlexSGD2.csv', index=1)


Results_Adam = {'train_loss': train_history.history['loss'],
                'train_accuracy': train_history.history['accuracy'],
                'val_loss': train_history.history['val_loss'],
                'val_accuracy': train_history.history['val_accuracy']}
results_df = pd.DataFrame(Results_Adam)
results_df.to_csv('ResultsAdam.csv', index=1)
'''

Accuracy Plot

def plot_accuracy(history):
  plt.figure(figsize = (10,6))
  plt.plot(history.history['accuracy'], color = 'blue', label = 'train')
  plt.plot(history.history['val_accuracy'], color = 'orange', label = 'val')
  plt.legend()
  plt.title('AlexNet Accuracy')
  plt.show()

plot_accuracy(train_history)

Loss Plot

def plot_loss(history):
  plt.figure(figsize = (10,6))
  plt.plot(history.history['loss'], color = 'blue', label = 'train')
  plt.plot(history.history['val_loss'], color = 'orange', label = 'val')
  plt.legend()
  plt.title('AlexNet Loss')
  plt.show()

plot_loss(train_history)

4.8 EfficientNet

Transfer learning and EfficientNet Introduction

Transfer Learning:

Transfer learning is a machine learning method where a pre-trained model is used as a starting point for developing a model on a different set of data. This is a useful technique because pre-trained models can be developed using massive datasets which requires a lot of resources. Then the pre-trained model can be integrated into the process of training a new model on a smaller dataset with less resources needed.

To accomplish this the pre-trained base model is frozen which means that it will not be trained on the new dataset. Fine tuning involves changing the top layers to fit the new dataset and classification labels.

EfficientNet:

EfficientNet is developed around the idea of using a compound coefficient to scale a convolutional neural network. Conventional approaches arbitrarily scale network dimensions (such as width, depth, and resolution). This method uniformly scales each dimension with a fixed set of scaling coefficients. The creators of this network found that the optimal formula for scaling multiple dimensions together was depth = 1.20, width = 1.10, and resolution = 1.15. This means when a CNN is scaled up the depth of layers will increase 20%, width will increase 10% and image resolution will increase 15%. This compound scaling method was used to scale from the base model EfficientNetB0 to the most complex model EfficientNetB7.

EfficientNet Experiments:

Model Input Features - Images: The input for the models is images that have been rescaled and image augmentation was performed.

Model Target - Classification: Cat or Dog Label

Workflow

• Preprocessing
  • We performed the following preprocessing steps:
    • Rescaled to the appropriate size for EfficientNet (refer to table)
    • Augmentation was performed on the training set (rotation, width and height shift, shear, zoom, horizontal flip).
• EfficientNet Models:
  • Refer to model diagram for model details.
  • The EfficientNet model was frozen (no training performed on it).
  • A top layer was added to train to our dataset.
  • Architecture for top layer:
    • Flatten
    • Dense - 1,024 hidden units and ReLu activation
    • Dropout - drop 50% of inputs to zero (prevents overfitting)
    • Dense - 1 node for classification and sigmoid activation.
  • Loss function: Binary Cross Entropy
  • Optimizer: RMSprop

Models:

Seven EfficientNet models were created:

1. EfficientNetB0
2. EfficientNetB0 with weight decay of 1e-6
3. EfficientNetB1 with weight decay of 1e-6
4. EfficientNetB2 with weight decay of 1e-6
5. EfficientNetB3 with weight decay of 1e-6
6. EfficientNetB4 with weight decay of 1e-6
7. EfficientNetB5 with weight decay of 1e-6

Loss Function

The EfficientNet models are using the Binary Cross Entropy loss function.

$$\mathcal{L}_{\text{BCE}}(y, \hat{y}) = -\frac{1}{N}\sum_{i=1}^{N} y_i \log(\hat{y_i}) + (1 - y_i) \log(1 - \hat{y_i})$$

Model Table:

Table of EfficientNet Base Models and the required image input resolution.

Workflow Diagram:

Diagram of EfficientNet Model Workflow: Images were resized to the appropriate size for each EfficientNet model. The weights of a pre-trained EfficientNet model were used (trained on ImageNet). The diagram desplays the architecture of EfficientNetB0. A top layer was added to integrate the pretrained model to our dataset and classification labels.

!pip install efficientnet

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting efficientnet
  Downloading efficientnet-1.1.1-py3-none-any.whl (18 kB)
Requirement already satisfied: scikit-image in /usr/local/lib/python3.9/dist-packages (from efficientnet) (0.19.3)
Collecting keras-applications<=1.0.8,>=1.0.7
  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 50.7/50.7 kB 1.6 MB/s eta 0:00:00
Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.9/dist-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.22.4)
Requirement already satisfied: h5py in /usr/local/lib/python3.9/dist-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (3.8.0)
Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.9/dist-packages (from scikit-image->efficientnet) (2023.4.12)
Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.9/dist-packages (from scikit-image->efficientnet) (3.1)
Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /usr/local/lib/python3.9/dist-packages (from scikit-image->efficientnet) (8.4.0)
Requirement already satisfied: imageio>=2.4.1 in /usr/local/lib/python3.9/dist-packages (from scikit-image->efficientnet) (2.25.1)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from scikit-image->efficientnet) (23.1)
Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.9/dist-packages (from scikit-image->efficientnet) (1.10.1)
Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from scikit-image->efficientnet) (1.4.1)
Installing collected packages: keras-applications, efficientnet
Successfully installed efficientnet-1.1.1 keras-applications-1.0.8

import tensorflow as tf 
from tensorflow.keras.preprocessing.image import ImageDataGenerator 
from tensorflow.keras import layers 
from tensorflow.keras import Model 
import matplotlib.pyplot as plt
import efficientnet.keras as efn
from tensorflow.keras.optimizers import RMSprop
from tensorflow.python.keras.layers import Dense, Flatten
import time
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

from google.colab import auth
from google.auth import default
from google.colab import drive
import gspread

auth.authenticate_user()
creds, _ = default()
gc = gspread.authorize(creds)
drive.mount('/content/drive')

Mounted at /content/drive

base_dir = '/content/drive/MyDrive/Data_Science_Final/aml'
train_dir = os.path.join(base_dir, 'training_set')
test_dir = os.path.join(base_dir, 'test_set')

# Directory with training cat pictures
train_cats_dir = os.path.join(train_dir, 'cats')

# Directory with training dog pictures
train_dogs_dir = os.path.join(train_dir, 'dogs')

# Directory with test cat pictures
test_cats_dir = os.path.join(test_dir, 'cats')

# Directory with test dog pictures
test_dogs_dir = os.path.join(test_dir, 'dogs')

#Display cat and dog images
# Set up matplotlib fig, and size it to fit 4x4 pics
nrows = 4
ncols = 4

fig = plt.gcf()
fig.set_size_inches(ncols*4, nrows*4)
pic_index = 100
train_cat_fnames = os.listdir(train_cats_dir)
train_dog_fnames = os.listdir(train_dogs_dir)


next_cat_pix = [os.path.join(train_cats_dir, fname) 
                for fname in train_cat_fnames[ pic_index-8:pic_index] 
               ]

next_dog_pix = [os.path.join(train_dogs_dir, fname) 
                for fname in train_dog_fnames[ pic_index-8:pic_index]
               ]

for i, img_path in enumerate(next_cat_pix+next_dog_pix):
  # Set up subplot; subplot indices start at 1
  sp = plt.subplot(nrows, ncols, i + 1)
  sp.axis('Off') # Don't show axes (or gridlines)

  img = mpimg.imread(img_path)
  plt.imshow(img)

plt.show()

EfficientNetB0

# Add rescaling and augmentation to ImageDataGenerator for the training set
train_datagen = ImageDataGenerator(rescale = 1./255., rotation_range = 40, width_shift_range = 0.2, height_shift_range = 0.2, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True, validation_split=0.1) # set validation split
# Rescale validation set. No augmentation on the validation set. 
validation_datagen = ImageDataGenerator(rescale = 1./255.,validation_split=0.1) # set validation split
#Read images directly from directory. 
train_generator = train_datagen.flow_from_directory(train_dir, seed = 42, shuffle = True, batch_size = 20, class_mode = 'binary', target_size = (224, 224), subset='training') #set as training data
validation_generator = validation_datagen.flow_from_directory(train_dir, seed = 42, shuffle = True, batch_size = 20, class_mode = 'binary', target_size = (224, 224), subset='validation') # same directory as training data. Set as validation data

Found 7205 images belonging to 2 classes.
Found 800 images belonging to 2 classes.

#Instantiates the EfficientNet architecture
base_model = efn.EfficientNetB0(input_shape = (224, 224, 3), include_top = False, weights = 'imagenet')

Downloading data from https://github.com/Callidior/keras-applications/releases/download/efficientnet/efficientnet-b0_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5
16804768/16804768 [==============================] - 2s 0us/step

# Set trainable attribute to false for all of the base model layers
for layer in base_model.layers:
    layer.trainable = False

#Build on top of existing base model. 
x = base_model.output
x = layers.Flatten()(x) #convert to 1D array
x = layers.Dense(1024, activation="relu")(x) #fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dropout(0.5)(x) #Drops 50% of inputs to zero at each training iteration (prevents overfitting)

# Add a final sigmoid layer with 1 node for classification output (probability between 0 and 1)
predictions = layers.Dense(1, activation="sigmoid")(x)
model_final = Model(inputs = base_model.input, outputs = predictions)

#Print model summary
from torchsummary import summary
model_sum = model_final.summary()

Model: "model"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               
                                )]                                                                
                                                                                                  
 stem_conv (Conv2D)             (None, 112, 112, 32  864         ['input_1[0][0]']                
                                )                                                                 
                                                                                                  
 stem_bn (BatchNormalization)   (None, 112, 112, 32  128         ['stem_conv[0][0]']              
                                )                                                                 
                                                                                                  
 stem_activation (Activation)   (None, 112, 112, 32  0           ['stem_bn[0][0]']                
                                )                                                                 
                                                                                                  
 block1a_dwconv (DepthwiseConv2  (None, 112, 112, 32  288        ['stem_activation[0][0]']        
 D)                             )                                                                 
                                                                                                  
 block1a_bn (BatchNormalization  (None, 112, 112, 32  128        ['block1a_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block1a_activation (Activation  (None, 112, 112, 32  0          ['block1a_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block1a_se_squeeze (GlobalAver  (None, 32)          0           ['block1a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block1a_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1a_se_squeeze[0][0]']     
                                                                                                  
 block1a_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1a_se_reshape[0][0]']     
                                                                                                  
 block1a_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1a_se_reduce[0][0]']      
                                                                                                  
 block1a_se_excite (Multiply)   (None, 112, 112, 32  0           ['block1a_activation[0][0]',     
                                )                                 'block1a_se_expand[0][0]']      
                                                                                                  
 block1a_project_conv (Conv2D)  (None, 112, 112, 16  512         ['block1a_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block1a_project_bn (BatchNorma  (None, 112, 112, 16  64         ['block1a_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block2a_expand_conv (Conv2D)   (None, 112, 112, 96  1536        ['block1a_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block2a_expand_bn (BatchNormal  (None, 112, 112, 96  384        ['block2a_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block2a_expand_activation (Act  (None, 112, 112, 96  0          ['block2a_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block2a_dwconv (DepthwiseConv2  (None, 56, 56, 96)  864         ['block2a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2a_bn (BatchNormalization  (None, 56, 56, 96)  384         ['block2a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2a_activation (Activation  (None, 56, 56, 96)  0           ['block2a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2a_se_squeeze (GlobalAver  (None, 96)          0           ['block2a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2a_se_reshape (Reshape)   (None, 1, 1, 96)     0           ['block2a_se_squeeze[0][0]']     
                                                                                                  
 block2a_se_reduce (Conv2D)     (None, 1, 1, 4)      388         ['block2a_se_reshape[0][0]']     
                                                                                                  
 block2a_se_expand (Conv2D)     (None, 1, 1, 96)     480         ['block2a_se_reduce[0][0]']      
                                                                                                  
 block2a_se_excite (Multiply)   (None, 56, 56, 96)   0           ['block2a_activation[0][0]',     
                                                                  'block2a_se_expand[0][0]']      
                                                                                                  
 block2a_project_conv (Conv2D)  (None, 56, 56, 24)   2304        ['block2a_se_excite[0][0]']      
                                                                                                  
 block2a_project_bn (BatchNorma  (None, 56, 56, 24)  96          ['block2a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2b_expand_conv (Conv2D)   (None, 56, 56, 144)  3456        ['block2a_project_bn[0][0]']     
                                                                                                  
 block2b_expand_bn (BatchNormal  (None, 56, 56, 144)  576        ['block2b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block2b_expand_activation (Act  (None, 56, 56, 144)  0          ['block2b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block2b_dwconv (DepthwiseConv2  (None, 56, 56, 144)  1296       ['block2b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2b_bn (BatchNormalization  (None, 56, 56, 144)  576        ['block2b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2b_activation (Activation  (None, 56, 56, 144)  0          ['block2b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2b_se_squeeze (GlobalAver  (None, 144)         0           ['block2b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2b_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2b_se_squeeze[0][0]']     
                                                                                                  
 block2b_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2b_se_reshape[0][0]']     
                                                                                                  
 block2b_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2b_se_reduce[0][0]']      
                                                                                                  
 block2b_se_excite (Multiply)   (None, 56, 56, 144)  0           ['block2b_activation[0][0]',     
                                                                  'block2b_se_expand[0][0]']      
                                                                                                  
 block2b_project_conv (Conv2D)  (None, 56, 56, 24)   3456        ['block2b_se_excite[0][0]']      
                                                                                                  
 block2b_project_bn (BatchNorma  (None, 56, 56, 24)  96          ['block2b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2b_drop (FixedDropout)    (None, 56, 56, 24)   0           ['block2b_project_bn[0][0]']     
                                                                                                  
 block2b_add (Add)              (None, 56, 56, 24)   0           ['block2b_drop[0][0]',           
                                                                  'block2a_project_bn[0][0]']     
                                                                                                  
 block3a_expand_conv (Conv2D)   (None, 56, 56, 144)  3456        ['block2b_add[0][0]']            
                                                                                                  
 block3a_expand_bn (BatchNormal  (None, 56, 56, 144)  576        ['block3a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3a_expand_activation (Act  (None, 56, 56, 144)  0          ['block3a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3a_dwconv (DepthwiseConv2  (None, 28, 28, 144)  3600       ['block3a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3a_bn (BatchNormalization  (None, 28, 28, 144)  576        ['block3a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3a_activation (Activation  (None, 28, 28, 144)  0          ['block3a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3a_se_squeeze (GlobalAver  (None, 144)         0           ['block3a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block3a_se_squeeze[0][0]']     
                                                                                                  
 block3a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block3a_se_reshape[0][0]']     
                                                                                                  
 block3a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block3a_se_reduce[0][0]']      
                                                                                                  
 block3a_se_excite (Multiply)   (None, 28, 28, 144)  0           ['block3a_activation[0][0]',     
                                                                  'block3a_se_expand[0][0]']      
                                                                                                  
 block3a_project_conv (Conv2D)  (None, 28, 28, 40)   5760        ['block3a_se_excite[0][0]']      
                                                                                                  
 block3a_project_bn (BatchNorma  (None, 28, 28, 40)  160         ['block3a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3b_expand_conv (Conv2D)   (None, 28, 28, 240)  9600        ['block3a_project_bn[0][0]']     
                                                                                                  
 block3b_expand_bn (BatchNormal  (None, 28, 28, 240)  960        ['block3b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3b_expand_activation (Act  (None, 28, 28, 240)  0          ['block3b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3b_dwconv (DepthwiseConv2  (None, 28, 28, 240)  6000       ['block3b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3b_bn (BatchNormalization  (None, 28, 28, 240)  960        ['block3b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3b_activation (Activation  (None, 28, 28, 240)  0          ['block3b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3b_se_squeeze (GlobalAver  (None, 240)         0           ['block3b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3b_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block3b_se_squeeze[0][0]']     
                                                                                                  
 block3b_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block3b_se_reshape[0][0]']     
                                                                                                  
 block3b_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block3b_se_reduce[0][0]']      
                                                                                                  
 block3b_se_excite (Multiply)   (None, 28, 28, 240)  0           ['block3b_activation[0][0]',     
                                                                  'block3b_se_expand[0][0]']      
                                                                                                  
 block3b_project_conv (Conv2D)  (None, 28, 28, 40)   9600        ['block3b_se_excite[0][0]']      
                                                                                                  
 block3b_project_bn (BatchNorma  (None, 28, 28, 40)  160         ['block3b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3b_drop (FixedDropout)    (None, 28, 28, 40)   0           ['block3b_project_bn[0][0]']     
                                                                                                  
 block3b_add (Add)              (None, 28, 28, 40)   0           ['block3b_drop[0][0]',           
                                                                  'block3a_project_bn[0][0]']     
                                                                                                  
 block4a_expand_conv (Conv2D)   (None, 28, 28, 240)  9600        ['block3b_add[0][0]']            
                                                                                                  
 block4a_expand_bn (BatchNormal  (None, 28, 28, 240)  960        ['block4a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4a_expand_activation (Act  (None, 28, 28, 240)  0          ['block4a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4a_dwconv (DepthwiseConv2  (None, 14, 14, 240)  2160       ['block4a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4a_bn (BatchNormalization  (None, 14, 14, 240)  960        ['block4a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4a_activation (Activation  (None, 14, 14, 240)  0          ['block4a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4a_se_squeeze (GlobalAver  (None, 240)         0           ['block4a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4a_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block4a_se_squeeze[0][0]']     
                                                                                                  
 block4a_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block4a_se_reshape[0][0]']     
                                                                                                  
 block4a_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block4a_se_reduce[0][0]']      
                                                                                                  
 block4a_se_excite (Multiply)   (None, 14, 14, 240)  0           ['block4a_activation[0][0]',     
                                                                  'block4a_se_expand[0][0]']      
                                                                                                  
 block4a_project_conv (Conv2D)  (None, 14, 14, 80)   19200       ['block4a_se_excite[0][0]']      
                                                                                                  
 block4a_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4b_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4a_project_bn[0][0]']     
                                                                                                  
 block4b_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block4b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4b_expand_activation (Act  (None, 14, 14, 480)  0          ['block4b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4b_dwconv (DepthwiseConv2  (None, 14, 14, 480)  4320       ['block4b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4b_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block4b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4b_activation (Activation  (None, 14, 14, 480)  0          ['block4b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4b_se_squeeze (GlobalAver  (None, 480)         0           ['block4b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4b_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4b_se_squeeze[0][0]']     
                                                                                                  
 block4b_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4b_se_reshape[0][0]']     
                                                                                                  
 block4b_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4b_se_reduce[0][0]']      
                                                                                                  
 block4b_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block4b_activation[0][0]',     
                                                                  'block4b_se_expand[0][0]']      
                                                                                                  
 block4b_project_conv (Conv2D)  (None, 14, 14, 80)   38400       ['block4b_se_excite[0][0]']      
                                                                                                  
 block4b_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4b_drop (FixedDropout)    (None, 14, 14, 80)   0           ['block4b_project_bn[0][0]']     
                                                                                                  
 block4b_add (Add)              (None, 14, 14, 80)   0           ['block4b_drop[0][0]',           
                                                                  'block4a_project_bn[0][0]']     
                                                                                                  
 block4c_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4b_add[0][0]']            
                                                                                                  
 block4c_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block4c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4c_expand_activation (Act  (None, 14, 14, 480)  0          ['block4c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4c_dwconv (DepthwiseConv2  (None, 14, 14, 480)  4320       ['block4c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4c_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block4c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4c_activation (Activation  (None, 14, 14, 480)  0          ['block4c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4c_se_squeeze (GlobalAver  (None, 480)         0           ['block4c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4c_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4c_se_squeeze[0][0]']     
                                                                                                  
 block4c_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4c_se_reshape[0][0]']     
                                                                                                  
 block4c_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4c_se_reduce[0][0]']      
                                                                                                  
 block4c_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block4c_activation[0][0]',     
                                                                  'block4c_se_expand[0][0]']      
                                                                                                  
 block4c_project_conv (Conv2D)  (None, 14, 14, 80)   38400       ['block4c_se_excite[0][0]']      
                                                                                                  
 block4c_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4c_drop (FixedDropout)    (None, 14, 14, 80)   0           ['block4c_project_bn[0][0]']     
                                                                                                  
 block4c_add (Add)              (None, 14, 14, 80)   0           ['block4c_drop[0][0]',           
                                                                  'block4b_add[0][0]']            
                                                                                                  
 block5a_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4c_add[0][0]']            
                                                                                                  
 block5a_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block5a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5a_expand_activation (Act  (None, 14, 14, 480)  0          ['block5a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5a_dwconv (DepthwiseConv2  (None, 14, 14, 480)  12000      ['block5a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5a_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block5a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5a_activation (Activation  (None, 14, 14, 480)  0          ['block5a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5a_se_squeeze (GlobalAver  (None, 480)         0           ['block5a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5a_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block5a_se_squeeze[0][0]']     
                                                                                                  
 block5a_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block5a_se_reshape[0][0]']     
                                                                                                  
 block5a_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block5a_se_reduce[0][0]']      
                                                                                                  
 block5a_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block5a_activation[0][0]',     
                                                                  'block5a_se_expand[0][0]']      
                                                                                                  
 block5a_project_conv (Conv2D)  (None, 14, 14, 112)  53760       ['block5a_se_excite[0][0]']      
                                                                                                  
 block5a_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5b_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5a_project_bn[0][0]']     
                                                                                                  
 block5b_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block5b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5b_expand_activation (Act  (None, 14, 14, 672)  0          ['block5b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5b_dwconv (DepthwiseConv2  (None, 14, 14, 672)  16800      ['block5b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5b_bn (BatchNormalization  (None, 14, 14, 672)  2688       ['block5b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5b_activation (Activation  (None, 14, 14, 672)  0          ['block5b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5b_se_squeeze (GlobalAver  (None, 672)         0           ['block5b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5b_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5b_se_squeeze[0][0]']     
                                                                                                  
 block5b_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5b_se_reshape[0][0]']     
                                                                                                  
 block5b_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5b_se_reduce[0][0]']      
                                                                                                  
 block5b_se_excite (Multiply)   (None, 14, 14, 672)  0           ['block5b_activation[0][0]',     
                                                                  'block5b_se_expand[0][0]']      
                                                                                                  
 block5b_project_conv (Conv2D)  (None, 14, 14, 112)  75264       ['block5b_se_excite[0][0]']      
                                                                                                  
 block5b_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5b_drop (FixedDropout)    (None, 14, 14, 112)  0           ['block5b_project_bn[0][0]']     
                                                                                                  
 block5b_add (Add)              (None, 14, 14, 112)  0           ['block5b_drop[0][0]',           
                                                                  'block5a_project_bn[0][0]']     
                                                                                                  
 block5c_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5b_add[0][0]']            
                                                                                                  
 block5c_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block5c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5c_expand_activation (Act  (None, 14, 14, 672)  0          ['block5c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5c_dwconv (DepthwiseConv2  (None, 14, 14, 672)  16800      ['block5c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5c_bn (BatchNormalization  (None, 14, 14, 672)  2688       ['block5c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5c_activation (Activation  (None, 14, 14, 672)  0          ['block5c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5c_se_squeeze (GlobalAver  (None, 672)         0           ['block5c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5c_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5c_se_squeeze[0][0]']     
                                                                                                  
 block5c_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5c_se_reshape[0][0]']     
                                                                                                  
 block5c_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5c_se_reduce[0][0]']      
                                                                                                  
 block5c_se_excite (Multiply)   (None, 14, 14, 672)  0           ['block5c_activation[0][0]',     
                                                                  'block5c_se_expand[0][0]']      
                                                                                                  
 block5c_project_conv (Conv2D)  (None, 14, 14, 112)  75264       ['block5c_se_excite[0][0]']      
                                                                                                  
 block5c_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5c_drop (FixedDropout)    (None, 14, 14, 112)  0           ['block5c_project_bn[0][0]']     
                                                                                                  
 block5c_add (Add)              (None, 14, 14, 112)  0           ['block5c_drop[0][0]',           
                                                                  'block5b_add[0][0]']            
                                                                                                  
 block6a_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5c_add[0][0]']            
                                                                                                  
 block6a_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block6a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6a_expand_activation (Act  (None, 14, 14, 672)  0          ['block6a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6a_dwconv (DepthwiseConv2  (None, 7, 7, 672)   16800       ['block6a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6a_bn (BatchNormalization  (None, 7, 7, 672)   2688        ['block6a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6a_activation (Activation  (None, 7, 7, 672)   0           ['block6a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6a_se_squeeze (GlobalAver  (None, 672)         0           ['block6a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6a_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block6a_se_squeeze[0][0]']     
                                                                                                  
 block6a_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block6a_se_reshape[0][0]']     
                                                                                                  
 block6a_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block6a_se_reduce[0][0]']      
                                                                                                  
 block6a_se_excite (Multiply)   (None, 7, 7, 672)    0           ['block6a_activation[0][0]',     
                                                                  'block6a_se_expand[0][0]']      
                                                                                                  
 block6a_project_conv (Conv2D)  (None, 7, 7, 192)    129024      ['block6a_se_excite[0][0]']      
                                                                                                  
 block6a_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6b_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6a_project_bn[0][0]']     
                                                                                                  
 block6b_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6b_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6b_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6b_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6b_activation (Activation  (None, 7, 7, 1152)  0           ['block6b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6b_se_squeeze (GlobalAver  (None, 1152)        0           ['block6b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6b_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6b_se_squeeze[0][0]']     
                                                                                                  
 block6b_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6b_se_reshape[0][0]']     
                                                                                                  
 block6b_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6b_se_reduce[0][0]']      
                                                                                                  
 block6b_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6b_activation[0][0]',     
                                                                  'block6b_se_expand[0][0]']      
                                                                                                  
 block6b_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6b_se_excite[0][0]']      
                                                                                                  
 block6b_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6b_drop (FixedDropout)    (None, 7, 7, 192)    0           ['block6b_project_bn[0][0]']     
                                                                                                  
 block6b_add (Add)              (None, 7, 7, 192)    0           ['block6b_drop[0][0]',           
                                                                  'block6a_project_bn[0][0]']     
                                                                                                  
 block6c_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6b_add[0][0]']            
                                                                                                  
 block6c_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6c_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6c_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6c_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6c_activation (Activation  (None, 7, 7, 1152)  0           ['block6c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6c_se_squeeze (GlobalAver  (None, 1152)        0           ['block6c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6c_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6c_se_squeeze[0][0]']     
                                                                                                  
 block6c_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6c_se_reshape[0][0]']     
                                                                                                  
 block6c_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6c_se_reduce[0][0]']      
                                                                                                  
 block6c_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6c_activation[0][0]',     
                                                                  'block6c_se_expand[0][0]']      
                                                                                                  
 block6c_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6c_se_excite[0][0]']      
                                                                                                  
 block6c_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6c_drop (FixedDropout)    (None, 7, 7, 192)    0           ['block6c_project_bn[0][0]']     
                                                                                                  
 block6c_add (Add)              (None, 7, 7, 192)    0           ['block6c_drop[0][0]',           
                                                                  'block6b_add[0][0]']            
                                                                                                  
 block6d_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6c_add[0][0]']            
                                                                                                  
 block6d_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6d_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6d_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6d_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6d_activation (Activation  (None, 7, 7, 1152)  0           ['block6d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6d_se_squeeze (GlobalAver  (None, 1152)        0           ['block6d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6d_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6d_se_squeeze[0][0]']     
                                                                                                  
 block6d_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6d_se_reshape[0][0]']     
                                                                                                  
 block6d_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6d_se_reduce[0][0]']      
                                                                                                  
 block6d_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6d_activation[0][0]',     
                                                                  'block6d_se_expand[0][0]']      
                                                                                                  
 block6d_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6d_se_excite[0][0]']      
                                                                                                  
 block6d_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6d_drop (FixedDropout)    (None, 7, 7, 192)    0           ['block6d_project_bn[0][0]']     
                                                                                                  
 block6d_add (Add)              (None, 7, 7, 192)    0           ['block6d_drop[0][0]',           
                                                                  'block6c_add[0][0]']            
                                                                                                  
 block7a_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6d_add[0][0]']            
                                                                                                  
 block7a_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block7a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block7a_expand_activation (Act  (None, 7, 7, 1152)  0           ['block7a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block7a_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  10368       ['block7a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block7a_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block7a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block7a_activation (Activation  (None, 7, 7, 1152)  0           ['block7a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block7a_se_squeeze (GlobalAver  (None, 1152)        0           ['block7a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block7a_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block7a_se_squeeze[0][0]']     
                                                                                                  
 block7a_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block7a_se_reshape[0][0]']     
                                                                                                  
 block7a_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block7a_se_reduce[0][0]']      
                                                                                                  
 block7a_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block7a_activation[0][0]',     
                                                                  'block7a_se_expand[0][0]']      
                                                                                                  
 block7a_project_conv (Conv2D)  (None, 7, 7, 320)    368640      ['block7a_se_excite[0][0]']      
                                                                                                  
 block7a_project_bn (BatchNorma  (None, 7, 7, 320)   1280        ['block7a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 top_conv (Conv2D)              (None, 7, 7, 1280)   409600      ['block7a_project_bn[0][0]']     
                                                                                                  
 top_bn (BatchNormalization)    (None, 7, 7, 1280)   5120        ['top_conv[0][0]']               
                                                                                                  
 top_activation (Activation)    (None, 7, 7, 1280)   0           ['top_bn[0][0]']                 
                                                                                                  
 flatten (Flatten)              (None, 62720)        0           ['top_activation[0][0]']         
                                                                                                  
 dense (Dense)                  (None, 1024)         64226304    ['flatten[0][0]']                
                                                                                                  
 dropout (Dropout)              (None, 1024)         0           ['dense[0][0]']                  
                                                                                                  
 dense_1 (Dense)                (None, 1)            1025        ['dropout[0][0]']                
                                                                                                  
==================================================================================================
Total params: 68,276,893
Trainable params: 64,227,329
Non-trainable params: 4,049,564
__________________________________________________________________________________________________

#get total parameters
model_params = model_final.count_params()

# Specify the optimizer, loss function and evaluation metrics. 
model_final.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001), metrics=['accuracy'])

t1 = time.time()
#train the model
eff_history = model_final.fit_generator(train_generator, validation_data = validation_generator, steps_per_epoch = 100, epochs = 10)
fit_time = time.time() - t1

<ipython-input-14-b7f31b017b18>:3: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  eff_history = model_final.fit_generator(train_generator, validation_data = validation_generator, steps_per_epoch = 100, epochs = 10)

Epoch 1/10
100/100 [==============================] - 739s 7s/step - loss: 0.3183 - accuracy: 0.9113 - val_loss: 0.0838 - val_accuracy: 0.9775
Epoch 2/10
100/100 [==============================] - 394s 4s/step - loss: 0.2768 - accuracy: 0.9275 - val_loss: 0.0838 - val_accuracy: 0.9825
Epoch 3/10
100/100 [==============================] - 284s 3s/step - loss: 0.3022 - accuracy: 0.9200 - val_loss: 0.0572 - val_accuracy: 0.9850
Epoch 4/10
100/100 [==============================] - 203s 2s/step - loss: 0.2296 - accuracy: 0.9451 - val_loss: 0.0614 - val_accuracy: 0.9875
Epoch 5/10
100/100 [==============================] - 154s 2s/step - loss: 0.2132 - accuracy: 0.9436 - val_loss: 0.1199 - val_accuracy: 0.9787
Epoch 6/10
100/100 [==============================] - 122s 1s/step - loss: 0.2683 - accuracy: 0.9420 - val_loss: 0.1028 - val_accuracy: 0.9837
Epoch 7/10
100/100 [==============================] - 91s 914ms/step - loss: 0.2781 - accuracy: 0.9315 - val_loss: 0.1754 - val_accuracy: 0.9787
Epoch 8/10
100/100 [==============================] - 87s 872ms/step - loss: 0.2735 - accuracy: 0.9400 - val_loss: 0.1717 - val_accuracy: 0.9837
Epoch 9/10
100/100 [==============================] - 63s 636ms/step - loss: 0.2872 - accuracy: 0.9375 - val_loss: 0.1673 - val_accuracy: 0.9787
Epoch 10/10
100/100 [==============================] - 54s 538ms/step - loss: 0.3210 - accuracy: 0.9515 - val_loss: 0.2309 - val_accuracy: 0.9775

# time it took to fit the model
print(fit_time) 

2195.61941075325

#Plot training and validation accuracy and loss for each epoch
acc = eff_history.history['accuracy']
val_acc = eff_history.history['val_accuracy']
loss = eff_history.history['loss']
val_loss = eff_history.history['val_loss']

epochs = range(1,len(acc) + 1)

plt.plot(epochs,acc,label = 'Training Accuracy')
plt.plot(epochs,val_acc,label = 'Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()
plt.figure()

plt.plot(epochs,loss,label = 'Training loss')
plt.plot(epochs,val_loss,label = 'Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()

plt.show()

# Test dataset 
test_datagen = ImageDataGenerator(rescale=1./255)

test_generator = test_datagen.flow_from_directory(
        test_dir,
        target_size=(224, 224),
        shuffle = False,
        class_mode='binary',
        batch_size=1)

Found 2023 images belonging to 2 classes.

#Get test length
filenames = test_generator.filenames
nb_samples = len(filenames)

#Predict on test set
predict = model_final.predict_generator(test_generator,steps = nb_samples)

<ipython-input-19-7710eff794cf>:2: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.
  predict = model_final.predict_generator(test_generator,steps = nb_samples)

#Get list of prediction results
pred_list = []
for i in predict:
    if i > 0.5:
        result = 1 #dog
        pred_list.append(result)
    else:
      result = 0 #cat
      pred_list.append(result)

#Create dataframe of image ID, image true label, image predicted label
import pandas as pd
image_ids = [name.split('/')[-1] for name in test_generator.filenames]
image_label = [name.split('/')[0] for name in test_generator.filenames]
data = {'id': image_ids, 'label':image_label, 'prediction':pred_list} 
data_df = pd.DataFrame(data)
data_df.label.replace(('cats', 'dogs'), (0, 1), inplace=True) # change cat and dog label to 0 or 1

#Get test accuracy score
from sklearn.metrics import accuracy_score, confusion_matrix
test_accuracy = accuracy_score(data_df['label'], data_df['prediction'])
print('Test Accuracy: ', round((test_accuracy * 100), 2), "%")

Test Accuracy:  97.13 %

from sklearn.metrics import classification_report
#Classification Report
print(classification_report(data_df['label'], data_df['prediction']))

              precision    recall  f1-score   support

           0       0.97      0.97      0.97      1011
           1       0.97      0.97      0.97      1012

    accuracy                           0.97      2023
   macro avg       0.97      0.97      0.97      2023
weighted avg       0.97      0.97      0.97      2023

#Create confusion matrix
import seaborn as sns
label = [0, 1] #0 = cat and 1 = dog
cm = confusion_matrix(data_df['label'], data_df['prediction'], labels = label)
#Plot
ax= plt.subplot()
sns.heatmap(cm, annot=True, fmt='g', ax=ax);
# labels, title and ticks
ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); 
ax.set_title('Confusion Matrix'); 
ax.xaxis.set_ticklabels(["Cat", "Dog"]); ax.yaxis.set_ticklabels(["Cat", "Dog"])

[Text(0, 0.5, 'Cat'), Text(0, 1.5, 'Dog')]

#Create experiment log
ExperimentLog  = pd.DataFrame(
    columns=[
        "Base Model",
        "Input Resolution",
        "Optimizer", 
        "Epochs", 
        "Training Accuracy",
        "Validation Accuracy",
        "Test Accuracy",
        "Fit Time",
        "Total Parameters"
    ]
)

ExperimentLog.loc[len(ExperimentLog)] = [
    "EfficientNet B0",
    224,
    "RMSprop", 
    10, 
    max(acc), 
    max(val_acc),
    test_accuracy,
    fit_time,
    model_params
]

ExperimentLog 

	Base Model	Input Resolution	Optimizer	Epochs	Training Accuracy	Validation Accuracy	Test Accuracy	Fit Time	Total Parameters
0	EfficientNet B0	224	RMSprop	10	0.9515	0.9875	0.97133	2195.619411	68276893

EfficientNetB0 with weight decay

# Specify the optimizer, loss function and evaluation metrics. 
model_final.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001, weight_decay=1e-6), metrics=['accuracy'])

t1 = time.time()
#train the model
eff_history = model_final.fit_generator(train_generator, validation_data = validation_generator, steps_per_epoch = 100, epochs = 10)
fit_time = time.time() - t1

<ipython-input-28-b7f31b017b18>:3: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  eff_history = model_final.fit_generator(train_generator, validation_data = validation_generator, steps_per_epoch = 100, epochs = 10)

Epoch 1/10
100/100 [==============================] - 50s 442ms/step - loss: 0.3316 - accuracy: 0.9420 - val_loss: 0.1937 - val_accuracy: 0.9862
Epoch 2/10
100/100 [==============================] - 48s 485ms/step - loss: 0.2205 - accuracy: 0.9530 - val_loss: 0.1539 - val_accuracy: 0.9875
Epoch 3/10
100/100 [==============================] - 40s 399ms/step - loss: 0.2557 - accuracy: 0.9490 - val_loss: 0.2016 - val_accuracy: 0.9850
Epoch 4/10
100/100 [==============================] - 37s 373ms/step - loss: 0.2714 - accuracy: 0.9455 - val_loss: 0.1942 - val_accuracy: 0.9850
Epoch 5/10
100/100 [==============================] - 34s 343ms/step - loss: 0.2824 - accuracy: 0.9455 - val_loss: 0.2047 - val_accuracy: 0.9837
Epoch 6/10
100/100 [==============================] - 34s 338ms/step - loss: 0.2901 - accuracy: 0.9530 - val_loss: 0.1585 - val_accuracy: 0.9825
Epoch 7/10
100/100 [==============================] - 32s 321ms/step - loss: 0.3372 - accuracy: 0.9485 - val_loss: 0.1716 - val_accuracy: 0.9850
Epoch 8/10
100/100 [==============================] - 32s 323ms/step - loss: 0.3549 - accuracy: 0.9490 - val_loss: 0.1882 - val_accuracy: 0.9862
Epoch 9/10
100/100 [==============================] - 32s 324ms/step - loss: 0.2235 - accuracy: 0.9520 - val_loss: 0.1747 - val_accuracy: 0.9862
Epoch 10/10
100/100 [==============================] - 31s 307ms/step - loss: 0.2472 - accuracy: 0.9562 - val_loss: 0.2063 - val_accuracy: 0.9837

# time it took to fit the model
print(fit_time) 

381.655277967453

#Plot training and validation accuracy and loss for each epoch
acc = eff_history.history['accuracy']
val_acc = eff_history.history['val_accuracy']
loss = eff_history.history['loss']
val_loss = eff_history.history['val_loss']

epochs = range(1,len(acc) + 1)

plt.plot(epochs,acc,label = 'Training Accuracy')
plt.plot(epochs,val_acc,label = 'Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()
plt.figure()

plt.plot(epochs,loss,label = 'Training loss')
plt.plot(epochs,val_loss,label = 'Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()

plt.show()

# Test dataset 
test_datagen = ImageDataGenerator(rescale=1./255)

test_generator = test_datagen.flow_from_directory(
        test_dir,
        target_size=(224, 224),
        shuffle = False,
        class_mode='binary',
        batch_size=1)

Found 2023 images belonging to 2 classes.

#Get test length
filenames = test_generator.filenames
nb_samples = len(filenames)

#Predict on test set
predict = model_final.predict_generator(test_generator,steps = nb_samples)

<ipython-input-33-7710eff794cf>:2: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.
  predict = model_final.predict_generator(test_generator,steps = nb_samples)

#Get list of prediction results
pred_list = []
for i in predict:
    if i > 0.5:
        result = 1 #dog
        pred_list.append(result)
    else:
      result = 0 #cat
      pred_list.append(result)

#Create dataframe of image ID, image true label, image predicted label
import pandas as pd
image_ids = [name.split('/')[-1] for name in test_generator.filenames]
image_label = [name.split('/')[0] for name in test_generator.filenames]
data = {'id': image_ids, 'label':image_label, 'prediction':pred_list} 
data_df = pd.DataFrame(data)
data_df.label.replace(('cats', 'dogs'), (0, 1), inplace=True) # change cat and dog label to 0 or 1

#Get test accuracy score
from sklearn.metrics import accuracy_score, confusion_matrix
test_accuracy = accuracy_score(data_df['label'], data_df['prediction'])
print('Test Accuracy: ', round((test_accuracy * 100), 2), "%")

Test Accuracy:  97.48 %

from sklearn.metrics import classification_report
#Classification Report
print(classification_report(data_df['label'], data_df['prediction']))

              precision    recall  f1-score   support

           0       0.97      0.98      0.97      1011
           1       0.98      0.97      0.97      1012

    accuracy                           0.97      2023
   macro avg       0.97      0.97      0.97      2023
weighted avg       0.97      0.97      0.97      2023

#Create confusion matrix
import seaborn as sns
label = [0, 1] #0 = cat and 1 = dog
cm = confusion_matrix(data_df['label'], data_df['prediction'], labels = label)
#Plot
ax= plt.subplot()
sns.heatmap(cm, annot=True, fmt='g', ax=ax);
# labels, title and ticks
ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); 
ax.set_title('Confusion Matrix'); 
ax.xaxis.set_ticklabels(["Cat", "Dog"]); ax.yaxis.set_ticklabels(["Cat", "Dog"])

[Text(0, 0.5, 'Cat'), Text(0, 1.5, 'Dog')]

ExperimentLog.loc[len(ExperimentLog)] = [
    "EfficientNet B0 with decay",
    224,
    "RMSprop", 
    10, 
    max(acc), 
    max(val_acc),
    test_accuracy,
    fit_time,
    model_params
]

ExperimentLog 

	Base Model	Input Resolution	Optimizer	Epochs	Training Accuracy	Validation Accuracy	Test Accuracy	Fit Time	Total Parameters
0	EfficientNet B0	224	RMSprop	10	0.951500	0.9875	0.97133	2195.619411	68276893
1	EfficientNet B0 with decay	224	RMSprop	10	0.956171	0.9875	0.97479	381.655278	68276893

EfficientNetB1

# Add rescaling and augmentation to ImageDataGenerator for the training set
train_datagen = ImageDataGenerator(rescale = 1./255., rotation_range = 40, width_shift_range = 0.2, height_shift_range = 0.2, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True, validation_split=0.1) # set validation split
# Rescale validation set. No augmentation on the validation set. 
validation_datagen = ImageDataGenerator(rescale = 1./255.,validation_split=0.1) # set validation split
#Read images directly from directory. 
train_generator = train_datagen.flow_from_directory(train_dir, seed = 42, shuffle = True, batch_size = 20, class_mode = 'binary', target_size = (240, 240), subset='training') #set as training data
validation_generator = validation_datagen.flow_from_directory(train_dir, seed = 42, shuffle = True, batch_size = 20, class_mode = 'binary', target_size = (240, 240), subset='validation') # same directory as training data. Set as validation data

Found 7205 images belonging to 2 classes.
Found 800 images belonging to 2 classes.

#Instantiates the EfficientNet architecture
base_model = efn.EfficientNetB1(input_shape = (240, 240, 3), include_top = False, weights = 'imagenet')

Downloading data from https://github.com/Callidior/keras-applications/releases/download/efficientnet/efficientnet-b1_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5
27164032/27164032 [==============================] - 6s 0us/step

# Set trainable attribute to false for all of the base model layers
for layer in base_model.layers:
    layer.trainable = False

#Build on top of existing base model.
x = base_model.output
x = layers.Flatten()(x) #convert to 1D array
x = layers.Dense(1024, activation="relu")(x) #fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dropout(0.5)(x) #Drops 50% of inputs to zero at each training iteration (prevents overfitting)

# Add a final sigmoid layer with 1 node for classification output (probability between 0 and 1)
predictions = layers.Dense(1, activation="sigmoid")(x)
model_final = Model(inputs = base_model.input, outputs = predictions)

#Print model summary
from torchsummary import summary
model_sum = model_final.summary()

Model: "model_1"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_2 (InputLayer)           [(None, 240, 240, 3  0           []                               
                                )]                                                                
                                                                                                  
 stem_conv (Conv2D)             (None, 120, 120, 32  864         ['input_2[0][0]']                
                                )                                                                 
                                                                                                  
 stem_bn (BatchNormalization)   (None, 120, 120, 32  128         ['stem_conv[0][0]']              
                                )                                                                 
                                                                                                  
 stem_activation (Activation)   (None, 120, 120, 32  0           ['stem_bn[0][0]']                
                                )                                                                 
                                                                                                  
 block1a_dwconv (DepthwiseConv2  (None, 120, 120, 32  288        ['stem_activation[0][0]']        
 D)                             )                                                                 
                                                                                                  
 block1a_bn (BatchNormalization  (None, 120, 120, 32  128        ['block1a_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block1a_activation (Activation  (None, 120, 120, 32  0          ['block1a_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block1a_se_squeeze (GlobalAver  (None, 32)          0           ['block1a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block1a_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1a_se_squeeze[0][0]']     
                                                                                                  
 block1a_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1a_se_reshape[0][0]']     
                                                                                                  
 block1a_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1a_se_reduce[0][0]']      
                                                                                                  
 block1a_se_excite (Multiply)   (None, 120, 120, 32  0           ['block1a_activation[0][0]',     
                                )                                 'block1a_se_expand[0][0]']      
                                                                                                  
 block1a_project_conv (Conv2D)  (None, 120, 120, 16  512         ['block1a_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block1a_project_bn (BatchNorma  (None, 120, 120, 16  64         ['block1a_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block1b_dwconv (DepthwiseConv2  (None, 120, 120, 16  144        ['block1a_project_bn[0][0]']     
 D)                             )                                                                 
                                                                                                  
 block1b_bn (BatchNormalization  (None, 120, 120, 16  64         ['block1b_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block1b_activation (Activation  (None, 120, 120, 16  0          ['block1b_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block1b_se_squeeze (GlobalAver  (None, 16)          0           ['block1b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block1b_se_reshape (Reshape)   (None, 1, 1, 16)     0           ['block1b_se_squeeze[0][0]']     
                                                                                                  
 block1b_se_reduce (Conv2D)     (None, 1, 1, 4)      68          ['block1b_se_reshape[0][0]']     
                                                                                                  
 block1b_se_expand (Conv2D)     (None, 1, 1, 16)     80          ['block1b_se_reduce[0][0]']      
                                                                                                  
 block1b_se_excite (Multiply)   (None, 120, 120, 16  0           ['block1b_activation[0][0]',     
                                )                                 'block1b_se_expand[0][0]']      
                                                                                                  
 block1b_project_conv (Conv2D)  (None, 120, 120, 16  256         ['block1b_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block1b_project_bn (BatchNorma  (None, 120, 120, 16  64         ['block1b_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block1b_drop (FixedDropout)    (None, 120, 120, 16  0           ['block1b_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block1b_add (Add)              (None, 120, 120, 16  0           ['block1b_drop[0][0]',           
                                )                                 'block1a_project_bn[0][0]']     
                                                                                                  
 block2a_expand_conv (Conv2D)   (None, 120, 120, 96  1536        ['block1b_add[0][0]']            
                                )                                                                 
                                                                                                  
 block2a_expand_bn (BatchNormal  (None, 120, 120, 96  384        ['block2a_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block2a_expand_activation (Act  (None, 120, 120, 96  0          ['block2a_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block2a_dwconv (DepthwiseConv2  (None, 60, 60, 96)  864         ['block2a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2a_bn (BatchNormalization  (None, 60, 60, 96)  384         ['block2a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2a_activation (Activation  (None, 60, 60, 96)  0           ['block2a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2a_se_squeeze (GlobalAver  (None, 96)          0           ['block2a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2a_se_reshape (Reshape)   (None, 1, 1, 96)     0           ['block2a_se_squeeze[0][0]']     
                                                                                                  
 block2a_se_reduce (Conv2D)     (None, 1, 1, 4)      388         ['block2a_se_reshape[0][0]']     
                                                                                                  
 block2a_se_expand (Conv2D)     (None, 1, 1, 96)     480         ['block2a_se_reduce[0][0]']      
                                                                                                  
 block2a_se_excite (Multiply)   (None, 60, 60, 96)   0           ['block2a_activation[0][0]',     
                                                                  'block2a_se_expand[0][0]']      
                                                                                                  
 block2a_project_conv (Conv2D)  (None, 60, 60, 24)   2304        ['block2a_se_excite[0][0]']      
                                                                                                  
 block2a_project_bn (BatchNorma  (None, 60, 60, 24)  96          ['block2a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2b_expand_conv (Conv2D)   (None, 60, 60, 144)  3456        ['block2a_project_bn[0][0]']     
                                                                                                  
 block2b_expand_bn (BatchNormal  (None, 60, 60, 144)  576        ['block2b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block2b_expand_activation (Act  (None, 60, 60, 144)  0          ['block2b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block2b_dwconv (DepthwiseConv2  (None, 60, 60, 144)  1296       ['block2b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2b_bn (BatchNormalization  (None, 60, 60, 144)  576        ['block2b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2b_activation (Activation  (None, 60, 60, 144)  0          ['block2b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2b_se_squeeze (GlobalAver  (None, 144)         0           ['block2b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2b_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2b_se_squeeze[0][0]']     
                                                                                                  
 block2b_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2b_se_reshape[0][0]']     
                                                                                                  
 block2b_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2b_se_reduce[0][0]']      
                                                                                                  
 block2b_se_excite (Multiply)   (None, 60, 60, 144)  0           ['block2b_activation[0][0]',     
                                                                  'block2b_se_expand[0][0]']      
                                                                                                  
 block2b_project_conv (Conv2D)  (None, 60, 60, 24)   3456        ['block2b_se_excite[0][0]']      
                                                                                                  
 block2b_project_bn (BatchNorma  (None, 60, 60, 24)  96          ['block2b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2b_drop (FixedDropout)    (None, 60, 60, 24)   0           ['block2b_project_bn[0][0]']     
                                                                                                  
 block2b_add (Add)              (None, 60, 60, 24)   0           ['block2b_drop[0][0]',           
                                                                  'block2a_project_bn[0][0]']     
                                                                                                  
 block2c_expand_conv (Conv2D)   (None, 60, 60, 144)  3456        ['block2b_add[0][0]']            
                                                                                                  
 block2c_expand_bn (BatchNormal  (None, 60, 60, 144)  576        ['block2c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block2c_expand_activation (Act  (None, 60, 60, 144)  0          ['block2c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block2c_dwconv (DepthwiseConv2  (None, 60, 60, 144)  1296       ['block2c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2c_bn (BatchNormalization  (None, 60, 60, 144)  576        ['block2c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2c_activation (Activation  (None, 60, 60, 144)  0          ['block2c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2c_se_squeeze (GlobalAver  (None, 144)         0           ['block2c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2c_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2c_se_squeeze[0][0]']     
                                                                                                  
 block2c_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2c_se_reshape[0][0]']     
                                                                                                  
 block2c_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2c_se_reduce[0][0]']      
                                                                                                  
 block2c_se_excite (Multiply)   (None, 60, 60, 144)  0           ['block2c_activation[0][0]',     
                                                                  'block2c_se_expand[0][0]']      
                                                                                                  
 block2c_project_conv (Conv2D)  (None, 60, 60, 24)   3456        ['block2c_se_excite[0][0]']      
                                                                                                  
 block2c_project_bn (BatchNorma  (None, 60, 60, 24)  96          ['block2c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2c_drop (FixedDropout)    (None, 60, 60, 24)   0           ['block2c_project_bn[0][0]']     
                                                                                                  
 block2c_add (Add)              (None, 60, 60, 24)   0           ['block2c_drop[0][0]',           
                                                                  'block2b_add[0][0]']            
                                                                                                  
 block3a_expand_conv (Conv2D)   (None, 60, 60, 144)  3456        ['block2c_add[0][0]']            
                                                                                                  
 block3a_expand_bn (BatchNormal  (None, 60, 60, 144)  576        ['block3a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3a_expand_activation (Act  (None, 60, 60, 144)  0          ['block3a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3a_dwconv (DepthwiseConv2  (None, 30, 30, 144)  3600       ['block3a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3a_bn (BatchNormalization  (None, 30, 30, 144)  576        ['block3a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3a_activation (Activation  (None, 30, 30, 144)  0          ['block3a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3a_se_squeeze (GlobalAver  (None, 144)         0           ['block3a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block3a_se_squeeze[0][0]']     
                                                                                                  
 block3a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block3a_se_reshape[0][0]']     
                                                                                                  
 block3a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block3a_se_reduce[0][0]']      
                                                                                                  
 block3a_se_excite (Multiply)   (None, 30, 30, 144)  0           ['block3a_activation[0][0]',     
                                                                  'block3a_se_expand[0][0]']      
                                                                                                  
 block3a_project_conv (Conv2D)  (None, 30, 30, 40)   5760        ['block3a_se_excite[0][0]']      
                                                                                                  
 block3a_project_bn (BatchNorma  (None, 30, 30, 40)  160         ['block3a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3b_expand_conv (Conv2D)   (None, 30, 30, 240)  9600        ['block3a_project_bn[0][0]']     
                                                                                                  
 block3b_expand_bn (BatchNormal  (None, 30, 30, 240)  960        ['block3b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3b_expand_activation (Act  (None, 30, 30, 240)  0          ['block3b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3b_dwconv (DepthwiseConv2  (None, 30, 30, 240)  6000       ['block3b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3b_bn (BatchNormalization  (None, 30, 30, 240)  960        ['block3b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3b_activation (Activation  (None, 30, 30, 240)  0          ['block3b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3b_se_squeeze (GlobalAver  (None, 240)         0           ['block3b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3b_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block3b_se_squeeze[0][0]']     
                                                                                                  
 block3b_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block3b_se_reshape[0][0]']     
                                                                                                  
 block3b_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block3b_se_reduce[0][0]']      
                                                                                                  
 block3b_se_excite (Multiply)   (None, 30, 30, 240)  0           ['block3b_activation[0][0]',     
                                                                  'block3b_se_expand[0][0]']      
                                                                                                  
 block3b_project_conv (Conv2D)  (None, 30, 30, 40)   9600        ['block3b_se_excite[0][0]']      
                                                                                                  
 block3b_project_bn (BatchNorma  (None, 30, 30, 40)  160         ['block3b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3b_drop (FixedDropout)    (None, 30, 30, 40)   0           ['block3b_project_bn[0][0]']     
                                                                                                  
 block3b_add (Add)              (None, 30, 30, 40)   0           ['block3b_drop[0][0]',           
                                                                  'block3a_project_bn[0][0]']     
                                                                                                  
 block3c_expand_conv (Conv2D)   (None, 30, 30, 240)  9600        ['block3b_add[0][0]']            
                                                                                                  
 block3c_expand_bn (BatchNormal  (None, 30, 30, 240)  960        ['block3c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3c_expand_activation (Act  (None, 30, 30, 240)  0          ['block3c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3c_dwconv (DepthwiseConv2  (None, 30, 30, 240)  6000       ['block3c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3c_bn (BatchNormalization  (None, 30, 30, 240)  960        ['block3c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3c_activation (Activation  (None, 30, 30, 240)  0          ['block3c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3c_se_squeeze (GlobalAver  (None, 240)         0           ['block3c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3c_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block3c_se_squeeze[0][0]']     
                                                                                                  
 block3c_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block3c_se_reshape[0][0]']     
                                                                                                  
 block3c_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block3c_se_reduce[0][0]']      
                                                                                                  
 block3c_se_excite (Multiply)   (None, 30, 30, 240)  0           ['block3c_activation[0][0]',     
                                                                  'block3c_se_expand[0][0]']      
                                                                                                  
 block3c_project_conv (Conv2D)  (None, 30, 30, 40)   9600        ['block3c_se_excite[0][0]']      
                                                                                                  
 block3c_project_bn (BatchNorma  (None, 30, 30, 40)  160         ['block3c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3c_drop (FixedDropout)    (None, 30, 30, 40)   0           ['block3c_project_bn[0][0]']     
                                                                                                  
 block3c_add (Add)              (None, 30, 30, 40)   0           ['block3c_drop[0][0]',           
                                                                  'block3b_add[0][0]']            
                                                                                                  
 block4a_expand_conv (Conv2D)   (None, 30, 30, 240)  9600        ['block3c_add[0][0]']            
                                                                                                  
 block4a_expand_bn (BatchNormal  (None, 30, 30, 240)  960        ['block4a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4a_expand_activation (Act  (None, 30, 30, 240)  0          ['block4a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4a_dwconv (DepthwiseConv2  (None, 15, 15, 240)  2160       ['block4a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4a_bn (BatchNormalization  (None, 15, 15, 240)  960        ['block4a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4a_activation (Activation  (None, 15, 15, 240)  0          ['block4a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4a_se_squeeze (GlobalAver  (None, 240)         0           ['block4a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4a_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block4a_se_squeeze[0][0]']     
                                                                                                  
 block4a_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block4a_se_reshape[0][0]']     
                                                                                                  
 block4a_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block4a_se_reduce[0][0]']      
                                                                                                  
 block4a_se_excite (Multiply)   (None, 15, 15, 240)  0           ['block4a_activation[0][0]',     
                                                                  'block4a_se_expand[0][0]']      
                                                                                                  
 block4a_project_conv (Conv2D)  (None, 15, 15, 80)   19200       ['block4a_se_excite[0][0]']      
                                                                                                  
 block4a_project_bn (BatchNorma  (None, 15, 15, 80)  320         ['block4a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4b_expand_conv (Conv2D)   (None, 15, 15, 480)  38400       ['block4a_project_bn[0][0]']     
                                                                                                  
 block4b_expand_bn (BatchNormal  (None, 15, 15, 480)  1920       ['block4b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4b_expand_activation (Act  (None, 15, 15, 480)  0          ['block4b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4b_dwconv (DepthwiseConv2  (None, 15, 15, 480)  4320       ['block4b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4b_bn (BatchNormalization  (None, 15, 15, 480)  1920       ['block4b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4b_activation (Activation  (None, 15, 15, 480)  0          ['block4b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4b_se_squeeze (GlobalAver  (None, 480)         0           ['block4b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4b_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4b_se_squeeze[0][0]']     
                                                                                                  
 block4b_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4b_se_reshape[0][0]']     
                                                                                                  
 block4b_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4b_se_reduce[0][0]']      
                                                                                                  
 block4b_se_excite (Multiply)   (None, 15, 15, 480)  0           ['block4b_activation[0][0]',     
                                                                  'block4b_se_expand[0][0]']      
                                                                                                  
 block4b_project_conv (Conv2D)  (None, 15, 15, 80)   38400       ['block4b_se_excite[0][0]']      
                                                                                                  
 block4b_project_bn (BatchNorma  (None, 15, 15, 80)  320         ['block4b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4b_drop (FixedDropout)    (None, 15, 15, 80)   0           ['block4b_project_bn[0][0]']     
                                                                                                  
 block4b_add (Add)              (None, 15, 15, 80)   0           ['block4b_drop[0][0]',           
                                                                  'block4a_project_bn[0][0]']     
                                                                                                  
 block4c_expand_conv (Conv2D)   (None, 15, 15, 480)  38400       ['block4b_add[0][0]']            
                                                                                                  
 block4c_expand_bn (BatchNormal  (None, 15, 15, 480)  1920       ['block4c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4c_expand_activation (Act  (None, 15, 15, 480)  0          ['block4c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4c_dwconv (DepthwiseConv2  (None, 15, 15, 480)  4320       ['block4c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4c_bn (BatchNormalization  (None, 15, 15, 480)  1920       ['block4c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4c_activation (Activation  (None, 15, 15, 480)  0          ['block4c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4c_se_squeeze (GlobalAver  (None, 480)         0           ['block4c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4c_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4c_se_squeeze[0][0]']     
                                                                                                  
 block4c_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4c_se_reshape[0][0]']     
                                                                                                  
 block4c_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4c_se_reduce[0][0]']      
                                                                                                  
 block4c_se_excite (Multiply)   (None, 15, 15, 480)  0           ['block4c_activation[0][0]',     
                                                                  'block4c_se_expand[0][0]']      
                                                                                                  
 block4c_project_conv (Conv2D)  (None, 15, 15, 80)   38400       ['block4c_se_excite[0][0]']      
                                                                                                  
 block4c_project_bn (BatchNorma  (None, 15, 15, 80)  320         ['block4c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4c_drop (FixedDropout)    (None, 15, 15, 80)   0           ['block4c_project_bn[0][0]']     
                                                                                                  
 block4c_add (Add)              (None, 15, 15, 80)   0           ['block4c_drop[0][0]',           
                                                                  'block4b_add[0][0]']            
                                                                                                  
 block4d_expand_conv (Conv2D)   (None, 15, 15, 480)  38400       ['block4c_add[0][0]']            
                                                                                                  
 block4d_expand_bn (BatchNormal  (None, 15, 15, 480)  1920       ['block4d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4d_expand_activation (Act  (None, 15, 15, 480)  0          ['block4d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4d_dwconv (DepthwiseConv2  (None, 15, 15, 480)  4320       ['block4d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4d_bn (BatchNormalization  (None, 15, 15, 480)  1920       ['block4d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4d_activation (Activation  (None, 15, 15, 480)  0          ['block4d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4d_se_squeeze (GlobalAver  (None, 480)         0           ['block4d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4d_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4d_se_squeeze[0][0]']     
                                                                                                  
 block4d_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4d_se_reshape[0][0]']     
                                                                                                  
 block4d_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4d_se_reduce[0][0]']      
                                                                                                  
 block4d_se_excite (Multiply)   (None, 15, 15, 480)  0           ['block4d_activation[0][0]',     
                                                                  'block4d_se_expand[0][0]']      
                                                                                                  
 block4d_project_conv (Conv2D)  (None, 15, 15, 80)   38400       ['block4d_se_excite[0][0]']      
                                                                                                  
 block4d_project_bn (BatchNorma  (None, 15, 15, 80)  320         ['block4d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4d_drop (FixedDropout)    (None, 15, 15, 80)   0           ['block4d_project_bn[0][0]']     
                                                                                                  
 block4d_add (Add)              (None, 15, 15, 80)   0           ['block4d_drop[0][0]',           
                                                                  'block4c_add[0][0]']            
                                                                                                  
 block5a_expand_conv (Conv2D)   (None, 15, 15, 480)  38400       ['block4d_add[0][0]']            
                                                                                                  
 block5a_expand_bn (BatchNormal  (None, 15, 15, 480)  1920       ['block5a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5a_expand_activation (Act  (None, 15, 15, 480)  0          ['block5a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5a_dwconv (DepthwiseConv2  (None, 15, 15, 480)  12000      ['block5a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5a_bn (BatchNormalization  (None, 15, 15, 480)  1920       ['block5a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5a_activation (Activation  (None, 15, 15, 480)  0          ['block5a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5a_se_squeeze (GlobalAver  (None, 480)         0           ['block5a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5a_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block5a_se_squeeze[0][0]']     
                                                                                                  
 block5a_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block5a_se_reshape[0][0]']     
                                                                                                  
 block5a_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block5a_se_reduce[0][0]']      
                                                                                                  
 block5a_se_excite (Multiply)   (None, 15, 15, 480)  0           ['block5a_activation[0][0]',     
                                                                  'block5a_se_expand[0][0]']      
                                                                                                  
 block5a_project_conv (Conv2D)  (None, 15, 15, 112)  53760       ['block5a_se_excite[0][0]']      
                                                                                                  
 block5a_project_bn (BatchNorma  (None, 15, 15, 112)  448        ['block5a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5b_expand_conv (Conv2D)   (None, 15, 15, 672)  75264       ['block5a_project_bn[0][0]']     
                                                                                                  
 block5b_expand_bn (BatchNormal  (None, 15, 15, 672)  2688       ['block5b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5b_expand_activation (Act  (None, 15, 15, 672)  0          ['block5b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5b_dwconv (DepthwiseConv2  (None, 15, 15, 672)  16800      ['block5b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5b_bn (BatchNormalization  (None, 15, 15, 672)  2688       ['block5b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5b_activation (Activation  (None, 15, 15, 672)  0          ['block5b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5b_se_squeeze (GlobalAver  (None, 672)         0           ['block5b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5b_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5b_se_squeeze[0][0]']     
                                                                                                  
 block5b_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5b_se_reshape[0][0]']     
                                                                                                  
 block5b_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5b_se_reduce[0][0]']      
                                                                                                  
 block5b_se_excite (Multiply)   (None, 15, 15, 672)  0           ['block5b_activation[0][0]',     
                                                                  'block5b_se_expand[0][0]']      
                                                                                                  
 block5b_project_conv (Conv2D)  (None, 15, 15, 112)  75264       ['block5b_se_excite[0][0]']      
                                                                                                  
 block5b_project_bn (BatchNorma  (None, 15, 15, 112)  448        ['block5b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5b_drop (FixedDropout)    (None, 15, 15, 112)  0           ['block5b_project_bn[0][0]']     
                                                                                                  
 block5b_add (Add)              (None, 15, 15, 112)  0           ['block5b_drop[0][0]',           
                                                                  'block5a_project_bn[0][0]']     
                                                                                                  
 block5c_expand_conv (Conv2D)   (None, 15, 15, 672)  75264       ['block5b_add[0][0]']            
                                                                                                  
 block5c_expand_bn (BatchNormal  (None, 15, 15, 672)  2688       ['block5c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5c_expand_activation (Act  (None, 15, 15, 672)  0          ['block5c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5c_dwconv (DepthwiseConv2  (None, 15, 15, 672)  16800      ['block5c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5c_bn (BatchNormalization  (None, 15, 15, 672)  2688       ['block5c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5c_activation (Activation  (None, 15, 15, 672)  0          ['block5c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5c_se_squeeze (GlobalAver  (None, 672)         0           ['block5c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5c_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5c_se_squeeze[0][0]']     
                                                                                                  
 block5c_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5c_se_reshape[0][0]']     
                                                                                                  
 block5c_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5c_se_reduce[0][0]']      
                                                                                                  
 block5c_se_excite (Multiply)   (None, 15, 15, 672)  0           ['block5c_activation[0][0]',     
                                                                  'block5c_se_expand[0][0]']      
                                                                                                  
 block5c_project_conv (Conv2D)  (None, 15, 15, 112)  75264       ['block5c_se_excite[0][0]']      
                                                                                                  
 block5c_project_bn (BatchNorma  (None, 15, 15, 112)  448        ['block5c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5c_drop (FixedDropout)    (None, 15, 15, 112)  0           ['block5c_project_bn[0][0]']     
                                                                                                  
 block5c_add (Add)              (None, 15, 15, 112)  0           ['block5c_drop[0][0]',           
                                                                  'block5b_add[0][0]']            
                                                                                                  
 block5d_expand_conv (Conv2D)   (None, 15, 15, 672)  75264       ['block5c_add[0][0]']            
                                                                                                  
 block5d_expand_bn (BatchNormal  (None, 15, 15, 672)  2688       ['block5d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5d_expand_activation (Act  (None, 15, 15, 672)  0          ['block5d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5d_dwconv (DepthwiseConv2  (None, 15, 15, 672)  16800      ['block5d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5d_bn (BatchNormalization  (None, 15, 15, 672)  2688       ['block5d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5d_activation (Activation  (None, 15, 15, 672)  0          ['block5d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5d_se_squeeze (GlobalAver  (None, 672)         0           ['block5d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5d_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5d_se_squeeze[0][0]']     
                                                                                                  
 block5d_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5d_se_reshape[0][0]']     
                                                                                                  
 block5d_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5d_se_reduce[0][0]']      
                                                                                                  
 block5d_se_excite (Multiply)   (None, 15, 15, 672)  0           ['block5d_activation[0][0]',     
                                                                  'block5d_se_expand[0][0]']      
                                                                                                  
 block5d_project_conv (Conv2D)  (None, 15, 15, 112)  75264       ['block5d_se_excite[0][0]']      
                                                                                                  
 block5d_project_bn (BatchNorma  (None, 15, 15, 112)  448        ['block5d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5d_drop (FixedDropout)    (None, 15, 15, 112)  0           ['block5d_project_bn[0][0]']     
                                                                                                  
 block5d_add (Add)              (None, 15, 15, 112)  0           ['block5d_drop[0][0]',           
                                                                  'block5c_add[0][0]']            
                                                                                                  
 block6a_expand_conv (Conv2D)   (None, 15, 15, 672)  75264       ['block5d_add[0][0]']            
                                                                                                  
 block6a_expand_bn (BatchNormal  (None, 15, 15, 672)  2688       ['block6a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6a_expand_activation (Act  (None, 15, 15, 672)  0          ['block6a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6a_dwconv (DepthwiseConv2  (None, 8, 8, 672)   16800       ['block6a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6a_bn (BatchNormalization  (None, 8, 8, 672)   2688        ['block6a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6a_activation (Activation  (None, 8, 8, 672)   0           ['block6a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6a_se_squeeze (GlobalAver  (None, 672)         0           ['block6a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6a_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block6a_se_squeeze[0][0]']     
                                                                                                  
 block6a_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block6a_se_reshape[0][0]']     
                                                                                                  
 block6a_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block6a_se_reduce[0][0]']      
                                                                                                  
 block6a_se_excite (Multiply)   (None, 8, 8, 672)    0           ['block6a_activation[0][0]',     
                                                                  'block6a_se_expand[0][0]']      
                                                                                                  
 block6a_project_conv (Conv2D)  (None, 8, 8, 192)    129024      ['block6a_se_excite[0][0]']      
                                                                                                  
 block6a_project_bn (BatchNorma  (None, 8, 8, 192)   768         ['block6a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6b_expand_conv (Conv2D)   (None, 8, 8, 1152)   221184      ['block6a_project_bn[0][0]']     
                                                                                                  
 block6b_expand_bn (BatchNormal  (None, 8, 8, 1152)  4608        ['block6b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6b_expand_activation (Act  (None, 8, 8, 1152)  0           ['block6b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6b_dwconv (DepthwiseConv2  (None, 8, 8, 1152)  28800       ['block6b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6b_bn (BatchNormalization  (None, 8, 8, 1152)  4608        ['block6b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6b_activation (Activation  (None, 8, 8, 1152)  0           ['block6b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6b_se_squeeze (GlobalAver  (None, 1152)        0           ['block6b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6b_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6b_se_squeeze[0][0]']     
                                                                                                  
 block6b_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6b_se_reshape[0][0]']     
                                                                                                  
 block6b_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6b_se_reduce[0][0]']      
                                                                                                  
 block6b_se_excite (Multiply)   (None, 8, 8, 1152)   0           ['block6b_activation[0][0]',     
                                                                  'block6b_se_expand[0][0]']      
                                                                                                  
 block6b_project_conv (Conv2D)  (None, 8, 8, 192)    221184      ['block6b_se_excite[0][0]']      
                                                                                                  
 block6b_project_bn (BatchNorma  (None, 8, 8, 192)   768         ['block6b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6b_drop (FixedDropout)    (None, 8, 8, 192)    0           ['block6b_project_bn[0][0]']     
                                                                                                  
 block6b_add (Add)              (None, 8, 8, 192)    0           ['block6b_drop[0][0]',           
                                                                  'block6a_project_bn[0][0]']     
                                                                                                  
 block6c_expand_conv (Conv2D)   (None, 8, 8, 1152)   221184      ['block6b_add[0][0]']            
                                                                                                  
 block6c_expand_bn (BatchNormal  (None, 8, 8, 1152)  4608        ['block6c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6c_expand_activation (Act  (None, 8, 8, 1152)  0           ['block6c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6c_dwconv (DepthwiseConv2  (None, 8, 8, 1152)  28800       ['block6c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6c_bn (BatchNormalization  (None, 8, 8, 1152)  4608        ['block6c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6c_activation (Activation  (None, 8, 8, 1152)  0           ['block6c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6c_se_squeeze (GlobalAver  (None, 1152)        0           ['block6c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6c_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6c_se_squeeze[0][0]']     
                                                                                                  
 block6c_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6c_se_reshape[0][0]']     
                                                                                                  
 block6c_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6c_se_reduce[0][0]']      
                                                                                                  
 block6c_se_excite (Multiply)   (None, 8, 8, 1152)   0           ['block6c_activation[0][0]',     
                                                                  'block6c_se_expand[0][0]']      
                                                                                                  
 block6c_project_conv (Conv2D)  (None, 8, 8, 192)    221184      ['block6c_se_excite[0][0]']      
                                                                                                  
 block6c_project_bn (BatchNorma  (None, 8, 8, 192)   768         ['block6c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6c_drop (FixedDropout)    (None, 8, 8, 192)    0           ['block6c_project_bn[0][0]']     
                                                                                                  
 block6c_add (Add)              (None, 8, 8, 192)    0           ['block6c_drop[0][0]',           
                                                                  'block6b_add[0][0]']            
                                                                                                  
 block6d_expand_conv (Conv2D)   (None, 8, 8, 1152)   221184      ['block6c_add[0][0]']            
                                                                                                  
 block6d_expand_bn (BatchNormal  (None, 8, 8, 1152)  4608        ['block6d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6d_expand_activation (Act  (None, 8, 8, 1152)  0           ['block6d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6d_dwconv (DepthwiseConv2  (None, 8, 8, 1152)  28800       ['block6d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6d_bn (BatchNormalization  (None, 8, 8, 1152)  4608        ['block6d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6d_activation (Activation  (None, 8, 8, 1152)  0           ['block6d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6d_se_squeeze (GlobalAver  (None, 1152)        0           ['block6d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6d_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6d_se_squeeze[0][0]']     
                                                                                                  
 block6d_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6d_se_reshape[0][0]']     
                                                                                                  
 block6d_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6d_se_reduce[0][0]']      
                                                                                                  
 block6d_se_excite (Multiply)   (None, 8, 8, 1152)   0           ['block6d_activation[0][0]',     
                                                                  'block6d_se_expand[0][0]']      
                                                                                                  
 block6d_project_conv (Conv2D)  (None, 8, 8, 192)    221184      ['block6d_se_excite[0][0]']      
                                                                                                  
 block6d_project_bn (BatchNorma  (None, 8, 8, 192)   768         ['block6d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6d_drop (FixedDropout)    (None, 8, 8, 192)    0           ['block6d_project_bn[0][0]']     
                                                                                                  
 block6d_add (Add)              (None, 8, 8, 192)    0           ['block6d_drop[0][0]',           
                                                                  'block6c_add[0][0]']            
                                                                                                  
 block6e_expand_conv (Conv2D)   (None, 8, 8, 1152)   221184      ['block6d_add[0][0]']            
                                                                                                  
 block6e_expand_bn (BatchNormal  (None, 8, 8, 1152)  4608        ['block6e_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6e_expand_activation (Act  (None, 8, 8, 1152)  0           ['block6e_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6e_dwconv (DepthwiseConv2  (None, 8, 8, 1152)  28800       ['block6e_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6e_bn (BatchNormalization  (None, 8, 8, 1152)  4608        ['block6e_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6e_activation (Activation  (None, 8, 8, 1152)  0           ['block6e_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6e_se_squeeze (GlobalAver  (None, 1152)        0           ['block6e_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6e_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6e_se_squeeze[0][0]']     
                                                                                                  
 block6e_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6e_se_reshape[0][0]']     
                                                                                                  
 block6e_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6e_se_reduce[0][0]']      
                                                                                                  
 block6e_se_excite (Multiply)   (None, 8, 8, 1152)   0           ['block6e_activation[0][0]',     
                                                                  'block6e_se_expand[0][0]']      
                                                                                                  
 block6e_project_conv (Conv2D)  (None, 8, 8, 192)    221184      ['block6e_se_excite[0][0]']      
                                                                                                  
 block6e_project_bn (BatchNorma  (None, 8, 8, 192)   768         ['block6e_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6e_drop (FixedDropout)    (None, 8, 8, 192)    0           ['block6e_project_bn[0][0]']     
                                                                                                  
 block6e_add (Add)              (None, 8, 8, 192)    0           ['block6e_drop[0][0]',           
                                                                  'block6d_add[0][0]']            
                                                                                                  
 block7a_expand_conv (Conv2D)   (None, 8, 8, 1152)   221184      ['block6e_add[0][0]']            
                                                                                                  
 block7a_expand_bn (BatchNormal  (None, 8, 8, 1152)  4608        ['block7a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block7a_expand_activation (Act  (None, 8, 8, 1152)  0           ['block7a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block7a_dwconv (DepthwiseConv2  (None, 8, 8, 1152)  10368       ['block7a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block7a_bn (BatchNormalization  (None, 8, 8, 1152)  4608        ['block7a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block7a_activation (Activation  (None, 8, 8, 1152)  0           ['block7a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block7a_se_squeeze (GlobalAver  (None, 1152)        0           ['block7a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block7a_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block7a_se_squeeze[0][0]']     
                                                                                                  
 block7a_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block7a_se_reshape[0][0]']     
                                                                                                  
 block7a_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block7a_se_reduce[0][0]']      
                                                                                                  
 block7a_se_excite (Multiply)   (None, 8, 8, 1152)   0           ['block7a_activation[0][0]',     
                                                                  'block7a_se_expand[0][0]']      
                                                                                                  
 block7a_project_conv (Conv2D)  (None, 8, 8, 320)    368640      ['block7a_se_excite[0][0]']      
                                                                                                  
 block7a_project_bn (BatchNorma  (None, 8, 8, 320)   1280        ['block7a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block7b_expand_conv (Conv2D)   (None, 8, 8, 1920)   614400      ['block7a_project_bn[0][0]']     
                                                                                                  
 block7b_expand_bn (BatchNormal  (None, 8, 8, 1920)  7680        ['block7b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block7b_expand_activation (Act  (None, 8, 8, 1920)  0           ['block7b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block7b_dwconv (DepthwiseConv2  (None, 8, 8, 1920)  17280       ['block7b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block7b_bn (BatchNormalization  (None, 8, 8, 1920)  7680        ['block7b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block7b_activation (Activation  (None, 8, 8, 1920)  0           ['block7b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block7b_se_squeeze (GlobalAver  (None, 1920)        0           ['block7b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block7b_se_reshape (Reshape)   (None, 1, 1, 1920)   0           ['block7b_se_squeeze[0][0]']     
                                                                                                  
 block7b_se_reduce (Conv2D)     (None, 1, 1, 80)     153680      ['block7b_se_reshape[0][0]']     
                                                                                                  
 block7b_se_expand (Conv2D)     (None, 1, 1, 1920)   155520      ['block7b_se_reduce[0][0]']      
                                                                                                  
 block7b_se_excite (Multiply)   (None, 8, 8, 1920)   0           ['block7b_activation[0][0]',     
                                                                  'block7b_se_expand[0][0]']      
                                                                                                  
 block7b_project_conv (Conv2D)  (None, 8, 8, 320)    614400      ['block7b_se_excite[0][0]']      
                                                                                                  
 block7b_project_bn (BatchNorma  (None, 8, 8, 320)   1280        ['block7b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block7b_drop (FixedDropout)    (None, 8, 8, 320)    0           ['block7b_project_bn[0][0]']     
                                                                                                  
 block7b_add (Add)              (None, 8, 8, 320)    0           ['block7b_drop[0][0]',           
                                                                  'block7a_project_bn[0][0]']     
                                                                                                  
 top_conv (Conv2D)              (None, 8, 8, 1280)   409600      ['block7b_add[0][0]']            
                                                                                                  
 top_bn (BatchNormalization)    (None, 8, 8, 1280)   5120        ['top_conv[0][0]']               
                                                                                                  
 top_activation (Activation)    (None, 8, 8, 1280)   0           ['top_bn[0][0]']                 
                                                                                                  
 flatten_1 (Flatten)            (None, 81920)        0           ['top_activation[0][0]']         
                                                                                                  
 dense_2 (Dense)                (None, 1024)         83887104    ['flatten_1[0][0]']              
                                                                                                  
 dropout_1 (Dropout)            (None, 1024)         0           ['dense_2[0][0]']                
                                                                                                  
 dense_3 (Dense)                (None, 1)            1025        ['dropout_1[0][0]']              
                                                                                                  
==================================================================================================
Total params: 90,463,361
Trainable params: 83,888,129
Non-trainable params: 6,575,232
__________________________________________________________________________________________________

#get total parameters
model_params = model_final.count_params()

# Specify the optimizer, loss function and evaluation metrics. 
model_final.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001, weight_decay=1e-6), metrics=['accuracy'])

t1 = time.time()
#train the model
eff_history = model_final.fit_generator(train_generator, validation_data = validation_generator, steps_per_epoch = 100, epochs = 10)
fit_time = time.time() - t1

<ipython-input-47-b7f31b017b18>:3: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  eff_history = model_final.fit_generator(train_generator, validation_data = validation_generator, steps_per_epoch = 100, epochs = 10)

Epoch 1/10
100/100 [==============================] - 46s 376ms/step - loss: 0.3899 - accuracy: 0.9175 - val_loss: 0.0736 - val_accuracy: 0.9837
Epoch 2/10
100/100 [==============================] - 33s 331ms/step - loss: 0.3238 - accuracy: 0.9295 - val_loss: 0.0540 - val_accuracy: 0.9862
Epoch 3/10
100/100 [==============================] - 34s 335ms/step - loss: 0.3243 - accuracy: 0.9415 - val_loss: 0.0531 - val_accuracy: 0.9887
Epoch 4/10
100/100 [==============================] - 33s 334ms/step - loss: 0.2570 - accuracy: 0.9510 - val_loss: 0.0419 - val_accuracy: 0.9912
Epoch 5/10
100/100 [==============================] - 33s 330ms/step - loss: 0.2258 - accuracy: 0.9505 - val_loss: 0.0689 - val_accuracy: 0.9887
Epoch 6/10
100/100 [==============================] - 35s 350ms/step - loss: 0.2548 - accuracy: 0.9510 - val_loss: 0.0650 - val_accuracy: 0.9875
Epoch 7/10
100/100 [==============================] - 33s 330ms/step - loss: 0.2618 - accuracy: 0.9466 - val_loss: 0.0591 - val_accuracy: 0.9850
Epoch 8/10
100/100 [==============================] - 33s 329ms/step - loss: 0.2577 - accuracy: 0.9440 - val_loss: 0.0594 - val_accuracy: 0.9850
Epoch 9/10
100/100 [==============================] - 33s 328ms/step - loss: 0.2216 - accuracy: 0.9515 - val_loss: 0.0670 - val_accuracy: 0.9862
Epoch 10/10
100/100 [==============================] - 33s 328ms/step - loss: 0.2298 - accuracy: 0.9597 - val_loss: 0.0616 - val_accuracy: 0.9887

# time it took to fit the model
print(fit_time)

349.48619532585144

#Plot training and validation accuracy and loss for each epoch
acc = eff_history.history['accuracy']
val_acc = eff_history.history['val_accuracy']
loss = eff_history.history['loss']
val_loss = eff_history.history['val_loss']

epochs = range(1,len(acc) + 1)

plt.plot(epochs,acc,label = 'Training Accuracy')
plt.plot(epochs,val_acc,label = 'Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()
plt.figure()

plt.plot(epochs,loss,label = 'Training loss')
plt.plot(epochs,val_loss,label = 'Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()

plt.show()

# Test dataset 
test_datagen = ImageDataGenerator(rescale=1./255)

test_generator = test_datagen.flow_from_directory(
        test_dir,
        target_size=(240, 240),
        shuffle = False,
        class_mode='binary',
        batch_size=1)

Found 2023 images belonging to 2 classes.

#Get test length
filenames = test_generator.filenames
nb_samples = len(filenames)

#Predict on test set
predict = model_final.predict_generator(test_generator,steps = nb_samples)

<ipython-input-52-7710eff794cf>:2: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.
  predict = model_final.predict_generator(test_generator,steps = nb_samples)

#Get list of prediction results
pred_list = []
for i in predict:
    if i > 0.5:
        result = 1 #dog
        pred_list.append(result)
    else:
      result = 0 #cat
      pred_list.append(result)

#Create dataframe of image ID, image true label, image predicted label
import pandas as pd
image_ids = [name.split('/')[-1] for name in test_generator.filenames]
image_label = [name.split('/')[0] for name in test_generator.filenames]
data = {'id': image_ids, 'label':image_label, 'prediction':pred_list} 
data_df = pd.DataFrame(data)
data_df.label.replace(('cats', 'dogs'), (0, 1), inplace=True) # change cat and dog label to 0 or 1

#Get test accuracy score
from sklearn.metrics import accuracy_score, confusion_matrix
test_accuracy = accuracy_score(data_df['label'], data_df['prediction'])
print('Test Accuracy: ', round((test_accuracy * 100), 2), "%")

Test Accuracy:  98.52 %

from sklearn.metrics import classification_report
#Classification Report
print(classification_report(data_df['label'], data_df['prediction']))

              precision    recall  f1-score   support

           0       0.99      0.98      0.99      1011
           1       0.98      0.99      0.99      1012

    accuracy                           0.99      2023
   macro avg       0.99      0.99      0.99      2023
weighted avg       0.99      0.99      0.99      2023

#Create confusion matrix
import seaborn as sns
label = [0, 1] #0 = cat and 1 = dog
cm = confusion_matrix(data_df['label'], data_df['prediction'], labels = label)
#Plot
ax= plt.subplot()
sns.heatmap(cm, annot=True, fmt='g', ax=ax);
# labels, title and ticks
ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); 
ax.set_title('Confusion Matrix'); 
ax.xaxis.set_ticklabels(["Cat", "Dog"]); ax.yaxis.set_ticklabels(["Cat", "Dog"])

[Text(0, 0.5, 'Cat'), Text(0, 1.5, 'Dog')]

ExperimentLog.loc[len(ExperimentLog)] = [
    "EfficientNet B1",
    240,
    "RMSprop", 
    10, 
    max(acc), 
    max(val_acc),
    test_accuracy,
    fit_time,
    model_params
]

ExperimentLog 

	Base Model	Input Resolution	Optimizer	Epochs	Training Accuracy	Validation Accuracy	Test Accuracy	Fit Time	Total Parameters
0	EfficientNet B0	224	RMSprop	10	0.951500	0.98750	0.971330	2195.619411	68276893
1	EfficientNet B0 with decay	224	RMSprop	10	0.956171	0.98750	0.974790	381.655278	68276893
2	EfficientNet B1	240	RMSprop	10	0.959698	0.99125	0.985171	349.486195	90463361

EfficientNetB2

# Add rescaling and augmentation to ImageDataGenerator for the training set
train_datagen = ImageDataGenerator(rescale = 1./255., rotation_range = 40, width_shift_range = 0.2, height_shift_range = 0.2, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True, validation_split=0.1) # set validation split
# Rescale validation set. No augmentation on the validation set. 
validation_datagen = ImageDataGenerator(rescale = 1./255.,validation_split=0.1) # set validation split
#Read images directly from directory. 
train_generator = train_datagen.flow_from_directory(train_dir, seed = 42, shuffle = True, batch_size = 20, class_mode = 'binary', target_size = (260, 260), subset='training') #set as training data
validation_generator = validation_datagen.flow_from_directory(train_dir, seed = 42, shuffle = True, batch_size = 20, class_mode = 'binary', target_size = (260, 260), subset='validation') # same directory as training data. Set as validation data

Found 7205 images belonging to 2 classes.
Found 800 images belonging to 2 classes.

#Instantiates the EfficientNet architecture
base_model = efn.EfficientNetB2(input_shape = (260, 260, 3), include_top = False, weights = 'imagenet')

Downloading data from https://github.com/Callidior/keras-applications/releases/download/efficientnet/efficientnet-b2_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5
31936256/31936256 [==============================] - 2s 0us/step

# Set trainable attribute to false for all of the base model layers
for layer in base_model.layers:
    layer.trainable = False

#Build on top of existing base model. 
x = base_model.output
x = layers.Flatten()(x) #convert to 1D array
x = layers.Dense(1024, activation="relu")(x) #fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dropout(0.5)(x) #Drops 50% of inputs to zero at each training iteration (prevents overfitting)

# Add a final sigmoid layer with 1 node for classification output (probability between 0 and 1)
predictions = layers.Dense(1, activation="sigmoid")(x)
model_final = Model(inputs = base_model.input, outputs = predictions)

#Print model summary
from torchsummary import summary
model_sum = model_final.summary()

Model: "model_2"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_3 (InputLayer)           [(None, 260, 260, 3  0           []                               
                                )]                                                                
                                                                                                  
 stem_conv (Conv2D)             (None, 130, 130, 32  864         ['input_3[0][0]']                
                                )                                                                 
                                                                                                  
 stem_bn (BatchNormalization)   (None, 130, 130, 32  128         ['stem_conv[0][0]']              
                                )                                                                 
                                                                                                  
 stem_activation (Activation)   (None, 130, 130, 32  0           ['stem_bn[0][0]']                
                                )                                                                 
                                                                                                  
 block1a_dwconv (DepthwiseConv2  (None, 130, 130, 32  288        ['stem_activation[0][0]']        
 D)                             )                                                                 
                                                                                                  
 block1a_bn (BatchNormalization  (None, 130, 130, 32  128        ['block1a_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block1a_activation (Activation  (None, 130, 130, 32  0          ['block1a_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block1a_se_squeeze (GlobalAver  (None, 32)          0           ['block1a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block1a_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1a_se_squeeze[0][0]']     
                                                                                                  
 block1a_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1a_se_reshape[0][0]']     
                                                                                                  
 block1a_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1a_se_reduce[0][0]']      
                                                                                                  
 block1a_se_excite (Multiply)   (None, 130, 130, 32  0           ['block1a_activation[0][0]',     
                                )                                 'block1a_se_expand[0][0]']      
                                                                                                  
 block1a_project_conv (Conv2D)  (None, 130, 130, 16  512         ['block1a_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block1a_project_bn (BatchNorma  (None, 130, 130, 16  64         ['block1a_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block1b_dwconv (DepthwiseConv2  (None, 130, 130, 16  144        ['block1a_project_bn[0][0]']     
 D)                             )                                                                 
                                                                                                  
 block1b_bn (BatchNormalization  (None, 130, 130, 16  64         ['block1b_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block1b_activation (Activation  (None, 130, 130, 16  0          ['block1b_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block1b_se_squeeze (GlobalAver  (None, 16)          0           ['block1b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block1b_se_reshape (Reshape)   (None, 1, 1, 16)     0           ['block1b_se_squeeze[0][0]']     
                                                                                                  
 block1b_se_reduce (Conv2D)     (None, 1, 1, 4)      68          ['block1b_se_reshape[0][0]']     
                                                                                                  
 block1b_se_expand (Conv2D)     (None, 1, 1, 16)     80          ['block1b_se_reduce[0][0]']      
                                                                                                  
 block1b_se_excite (Multiply)   (None, 130, 130, 16  0           ['block1b_activation[0][0]',     
                                )                                 'block1b_se_expand[0][0]']      
                                                                                                  
 block1b_project_conv (Conv2D)  (None, 130, 130, 16  256         ['block1b_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block1b_project_bn (BatchNorma  (None, 130, 130, 16  64         ['block1b_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block1b_drop (FixedDropout)    (None, 130, 130, 16  0           ['block1b_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block1b_add (Add)              (None, 130, 130, 16  0           ['block1b_drop[0][0]',           
                                )                                 'block1a_project_bn[0][0]']     
                                                                                                  
 block2a_expand_conv (Conv2D)   (None, 130, 130, 96  1536        ['block1b_add[0][0]']            
                                )                                                                 
                                                                                                  
 block2a_expand_bn (BatchNormal  (None, 130, 130, 96  384        ['block2a_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block2a_expand_activation (Act  (None, 130, 130, 96  0          ['block2a_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block2a_dwconv (DepthwiseConv2  (None, 65, 65, 96)  864         ['block2a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2a_bn (BatchNormalization  (None, 65, 65, 96)  384         ['block2a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2a_activation (Activation  (None, 65, 65, 96)  0           ['block2a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2a_se_squeeze (GlobalAver  (None, 96)          0           ['block2a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2a_se_reshape (Reshape)   (None, 1, 1, 96)     0           ['block2a_se_squeeze[0][0]']     
                                                                                                  
 block2a_se_reduce (Conv2D)     (None, 1, 1, 4)      388         ['block2a_se_reshape[0][0]']     
                                                                                                  
 block2a_se_expand (Conv2D)     (None, 1, 1, 96)     480         ['block2a_se_reduce[0][0]']      
                                                                                                  
 block2a_se_excite (Multiply)   (None, 65, 65, 96)   0           ['block2a_activation[0][0]',     
                                                                  'block2a_se_expand[0][0]']      
                                                                                                  
 block2a_project_conv (Conv2D)  (None, 65, 65, 24)   2304        ['block2a_se_excite[0][0]']      
                                                                                                  
 block2a_project_bn (BatchNorma  (None, 65, 65, 24)  96          ['block2a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2b_expand_conv (Conv2D)   (None, 65, 65, 144)  3456        ['block2a_project_bn[0][0]']     
                                                                                                  
 block2b_expand_bn (BatchNormal  (None, 65, 65, 144)  576        ['block2b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block2b_expand_activation (Act  (None, 65, 65, 144)  0          ['block2b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block2b_dwconv (DepthwiseConv2  (None, 65, 65, 144)  1296       ['block2b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2b_bn (BatchNormalization  (None, 65, 65, 144)  576        ['block2b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2b_activation (Activation  (None, 65, 65, 144)  0          ['block2b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2b_se_squeeze (GlobalAver  (None, 144)         0           ['block2b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2b_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2b_se_squeeze[0][0]']     
                                                                                                  
 block2b_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2b_se_reshape[0][0]']     
                                                                                                  
 block2b_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2b_se_reduce[0][0]']      
                                                                                                  
 block2b_se_excite (Multiply)   (None, 65, 65, 144)  0           ['block2b_activation[0][0]',     
                                                                  'block2b_se_expand[0][0]']      
                                                                                                  
 block2b_project_conv (Conv2D)  (None, 65, 65, 24)   3456        ['block2b_se_excite[0][0]']      
                                                                                                  
 block2b_project_bn (BatchNorma  (None, 65, 65, 24)  96          ['block2b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2b_drop (FixedDropout)    (None, 65, 65, 24)   0           ['block2b_project_bn[0][0]']     
                                                                                                  
 block2b_add (Add)              (None, 65, 65, 24)   0           ['block2b_drop[0][0]',           
                                                                  'block2a_project_bn[0][0]']     
                                                                                                  
 block2c_expand_conv (Conv2D)   (None, 65, 65, 144)  3456        ['block2b_add[0][0]']            
                                                                                                  
 block2c_expand_bn (BatchNormal  (None, 65, 65, 144)  576        ['block2c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block2c_expand_activation (Act  (None, 65, 65, 144)  0          ['block2c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block2c_dwconv (DepthwiseConv2  (None, 65, 65, 144)  1296       ['block2c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2c_bn (BatchNormalization  (None, 65, 65, 144)  576        ['block2c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2c_activation (Activation  (None, 65, 65, 144)  0          ['block2c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2c_se_squeeze (GlobalAver  (None, 144)         0           ['block2c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2c_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2c_se_squeeze[0][0]']     
                                                                                                  
 block2c_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2c_se_reshape[0][0]']     
                                                                                                  
 block2c_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2c_se_reduce[0][0]']      
                                                                                                  
 block2c_se_excite (Multiply)   (None, 65, 65, 144)  0           ['block2c_activation[0][0]',     
                                                                  'block2c_se_expand[0][0]']      
                                                                                                  
 block2c_project_conv (Conv2D)  (None, 65, 65, 24)   3456        ['block2c_se_excite[0][0]']      
                                                                                                  
 block2c_project_bn (BatchNorma  (None, 65, 65, 24)  96          ['block2c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2c_drop (FixedDropout)    (None, 65, 65, 24)   0           ['block2c_project_bn[0][0]']     
                                                                                                  
 block2c_add (Add)              (None, 65, 65, 24)   0           ['block2c_drop[0][0]',           
                                                                  'block2b_add[0][0]']            
                                                                                                  
 block3a_expand_conv (Conv2D)   (None, 65, 65, 144)  3456        ['block2c_add[0][0]']            
                                                                                                  
 block3a_expand_bn (BatchNormal  (None, 65, 65, 144)  576        ['block3a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3a_expand_activation (Act  (None, 65, 65, 144)  0          ['block3a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3a_dwconv (DepthwiseConv2  (None, 33, 33, 144)  3600       ['block3a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3a_bn (BatchNormalization  (None, 33, 33, 144)  576        ['block3a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3a_activation (Activation  (None, 33, 33, 144)  0          ['block3a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3a_se_squeeze (GlobalAver  (None, 144)         0           ['block3a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block3a_se_squeeze[0][0]']     
                                                                                                  
 block3a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block3a_se_reshape[0][0]']     
                                                                                                  
 block3a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block3a_se_reduce[0][0]']      
                                                                                                  
 block3a_se_excite (Multiply)   (None, 33, 33, 144)  0           ['block3a_activation[0][0]',     
                                                                  'block3a_se_expand[0][0]']      
                                                                                                  
 block3a_project_conv (Conv2D)  (None, 33, 33, 48)   6912        ['block3a_se_excite[0][0]']      
                                                                                                  
 block3a_project_bn (BatchNorma  (None, 33, 33, 48)  192         ['block3a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3b_expand_conv (Conv2D)   (None, 33, 33, 288)  13824       ['block3a_project_bn[0][0]']     
                                                                                                  
 block3b_expand_bn (BatchNormal  (None, 33, 33, 288)  1152       ['block3b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3b_expand_activation (Act  (None, 33, 33, 288)  0          ['block3b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3b_dwconv (DepthwiseConv2  (None, 33, 33, 288)  7200       ['block3b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3b_bn (BatchNormalization  (None, 33, 33, 288)  1152       ['block3b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3b_activation (Activation  (None, 33, 33, 288)  0          ['block3b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3b_se_squeeze (GlobalAver  (None, 288)         0           ['block3b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3b_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block3b_se_squeeze[0][0]']     
                                                                                                  
 block3b_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block3b_se_reshape[0][0]']     
                                                                                                  
 block3b_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block3b_se_reduce[0][0]']      
                                                                                                  
 block3b_se_excite (Multiply)   (None, 33, 33, 288)  0           ['block3b_activation[0][0]',     
                                                                  'block3b_se_expand[0][0]']      
                                                                                                  
 block3b_project_conv (Conv2D)  (None, 33, 33, 48)   13824       ['block3b_se_excite[0][0]']      
                                                                                                  
 block3b_project_bn (BatchNorma  (None, 33, 33, 48)  192         ['block3b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3b_drop (FixedDropout)    (None, 33, 33, 48)   0           ['block3b_project_bn[0][0]']     
                                                                                                  
 block3b_add (Add)              (None, 33, 33, 48)   0           ['block3b_drop[0][0]',           
                                                                  'block3a_project_bn[0][0]']     
                                                                                                  
 block3c_expand_conv (Conv2D)   (None, 33, 33, 288)  13824       ['block3b_add[0][0]']            
                                                                                                  
 block3c_expand_bn (BatchNormal  (None, 33, 33, 288)  1152       ['block3c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3c_expand_activation (Act  (None, 33, 33, 288)  0          ['block3c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3c_dwconv (DepthwiseConv2  (None, 33, 33, 288)  7200       ['block3c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3c_bn (BatchNormalization  (None, 33, 33, 288)  1152       ['block3c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3c_activation (Activation  (None, 33, 33, 288)  0          ['block3c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3c_se_squeeze (GlobalAver  (None, 288)         0           ['block3c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3c_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block3c_se_squeeze[0][0]']     
                                                                                                  
 block3c_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block3c_se_reshape[0][0]']     
                                                                                                  
 block3c_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block3c_se_reduce[0][0]']      
                                                                                                  
 block3c_se_excite (Multiply)   (None, 33, 33, 288)  0           ['block3c_activation[0][0]',     
                                                                  'block3c_se_expand[0][0]']      
                                                                                                  
 block3c_project_conv (Conv2D)  (None, 33, 33, 48)   13824       ['block3c_se_excite[0][0]']      
                                                                                                  
 block3c_project_bn (BatchNorma  (None, 33, 33, 48)  192         ['block3c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3c_drop (FixedDropout)    (None, 33, 33, 48)   0           ['block3c_project_bn[0][0]']     
                                                                                                  
 block3c_add (Add)              (None, 33, 33, 48)   0           ['block3c_drop[0][0]',           
                                                                  'block3b_add[0][0]']            
                                                                                                  
 block4a_expand_conv (Conv2D)   (None, 33, 33, 288)  13824       ['block3c_add[0][0]']            
                                                                                                  
 block4a_expand_bn (BatchNormal  (None, 33, 33, 288)  1152       ['block4a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4a_expand_activation (Act  (None, 33, 33, 288)  0          ['block4a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4a_dwconv (DepthwiseConv2  (None, 17, 17, 288)  2592       ['block4a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4a_bn (BatchNormalization  (None, 17, 17, 288)  1152       ['block4a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4a_activation (Activation  (None, 17, 17, 288)  0          ['block4a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4a_se_squeeze (GlobalAver  (None, 288)         0           ['block4a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4a_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block4a_se_squeeze[0][0]']     
                                                                                                  
 block4a_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block4a_se_reshape[0][0]']     
                                                                                                  
 block4a_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block4a_se_reduce[0][0]']      
                                                                                                  
 block4a_se_excite (Multiply)   (None, 17, 17, 288)  0           ['block4a_activation[0][0]',     
                                                                  'block4a_se_expand[0][0]']      
                                                                                                  
 block4a_project_conv (Conv2D)  (None, 17, 17, 88)   25344       ['block4a_se_excite[0][0]']      
                                                                                                  
 block4a_project_bn (BatchNorma  (None, 17, 17, 88)  352         ['block4a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4b_expand_conv (Conv2D)   (None, 17, 17, 528)  46464       ['block4a_project_bn[0][0]']     
                                                                                                  
 block4b_expand_bn (BatchNormal  (None, 17, 17, 528)  2112       ['block4b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4b_expand_activation (Act  (None, 17, 17, 528)  0          ['block4b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4b_dwconv (DepthwiseConv2  (None, 17, 17, 528)  4752       ['block4b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4b_bn (BatchNormalization  (None, 17, 17, 528)  2112       ['block4b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4b_activation (Activation  (None, 17, 17, 528)  0          ['block4b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4b_se_squeeze (GlobalAver  (None, 528)         0           ['block4b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4b_se_reshape (Reshape)   (None, 1, 1, 528)    0           ['block4b_se_squeeze[0][0]']     
                                                                                                  
 block4b_se_reduce (Conv2D)     (None, 1, 1, 22)     11638       ['block4b_se_reshape[0][0]']     
                                                                                                  
 block4b_se_expand (Conv2D)     (None, 1, 1, 528)    12144       ['block4b_se_reduce[0][0]']      
                                                                                                  
 block4b_se_excite (Multiply)   (None, 17, 17, 528)  0           ['block4b_activation[0][0]',     
                                                                  'block4b_se_expand[0][0]']      
                                                                                                  
 block4b_project_conv (Conv2D)  (None, 17, 17, 88)   46464       ['block4b_se_excite[0][0]']      
                                                                                                  
 block4b_project_bn (BatchNorma  (None, 17, 17, 88)  352         ['block4b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4b_drop (FixedDropout)    (None, 17, 17, 88)   0           ['block4b_project_bn[0][0]']     
                                                                                                  
 block4b_add (Add)              (None, 17, 17, 88)   0           ['block4b_drop[0][0]',           
                                                                  'block4a_project_bn[0][0]']     
                                                                                                  
 block4c_expand_conv (Conv2D)   (None, 17, 17, 528)  46464       ['block4b_add[0][0]']            
                                                                                                  
 block4c_expand_bn (BatchNormal  (None, 17, 17, 528)  2112       ['block4c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4c_expand_activation (Act  (None, 17, 17, 528)  0          ['block4c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4c_dwconv (DepthwiseConv2  (None, 17, 17, 528)  4752       ['block4c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4c_bn (BatchNormalization  (None, 17, 17, 528)  2112       ['block4c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4c_activation (Activation  (None, 17, 17, 528)  0          ['block4c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4c_se_squeeze (GlobalAver  (None, 528)         0           ['block4c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4c_se_reshape (Reshape)   (None, 1, 1, 528)    0           ['block4c_se_squeeze[0][0]']     
                                                                                                  
 block4c_se_reduce (Conv2D)     (None, 1, 1, 22)     11638       ['block4c_se_reshape[0][0]']     
                                                                                                  
 block4c_se_expand (Conv2D)     (None, 1, 1, 528)    12144       ['block4c_se_reduce[0][0]']      
                                                                                                  
 block4c_se_excite (Multiply)   (None, 17, 17, 528)  0           ['block4c_activation[0][0]',     
                                                                  'block4c_se_expand[0][0]']      
                                                                                                  
 block4c_project_conv (Conv2D)  (None, 17, 17, 88)   46464       ['block4c_se_excite[0][0]']      
                                                                                                  
 block4c_project_bn (BatchNorma  (None, 17, 17, 88)  352         ['block4c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4c_drop (FixedDropout)    (None, 17, 17, 88)   0           ['block4c_project_bn[0][0]']     
                                                                                                  
 block4c_add (Add)              (None, 17, 17, 88)   0           ['block4c_drop[0][0]',           
                                                                  'block4b_add[0][0]']            
                                                                                                  
 block4d_expand_conv (Conv2D)   (None, 17, 17, 528)  46464       ['block4c_add[0][0]']            
                                                                                                  
 block4d_expand_bn (BatchNormal  (None, 17, 17, 528)  2112       ['block4d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4d_expand_activation (Act  (None, 17, 17, 528)  0          ['block4d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4d_dwconv (DepthwiseConv2  (None, 17, 17, 528)  4752       ['block4d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4d_bn (BatchNormalization  (None, 17, 17, 528)  2112       ['block4d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4d_activation (Activation  (None, 17, 17, 528)  0          ['block4d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4d_se_squeeze (GlobalAver  (None, 528)         0           ['block4d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4d_se_reshape (Reshape)   (None, 1, 1, 528)    0           ['block4d_se_squeeze[0][0]']     
                                                                                                  
 block4d_se_reduce (Conv2D)     (None, 1, 1, 22)     11638       ['block4d_se_reshape[0][0]']     
                                                                                                  
 block4d_se_expand (Conv2D)     (None, 1, 1, 528)    12144       ['block4d_se_reduce[0][0]']      
                                                                                                  
 block4d_se_excite (Multiply)   (None, 17, 17, 528)  0           ['block4d_activation[0][0]',     
                                                                  'block4d_se_expand[0][0]']      
                                                                                                  
 block4d_project_conv (Conv2D)  (None, 17, 17, 88)   46464       ['block4d_se_excite[0][0]']      
                                                                                                  
 block4d_project_bn (BatchNorma  (None, 17, 17, 88)  352         ['block4d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4d_drop (FixedDropout)    (None, 17, 17, 88)   0           ['block4d_project_bn[0][0]']     
                                                                                                  
 block4d_add (Add)              (None, 17, 17, 88)   0           ['block4d_drop[0][0]',           
                                                                  'block4c_add[0][0]']            
                                                                                                  
 block5a_expand_conv (Conv2D)   (None, 17, 17, 528)  46464       ['block4d_add[0][0]']            
                                                                                                  
 block5a_expand_bn (BatchNormal  (None, 17, 17, 528)  2112       ['block5a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5a_expand_activation (Act  (None, 17, 17, 528)  0          ['block5a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5a_dwconv (DepthwiseConv2  (None, 17, 17, 528)  13200      ['block5a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5a_bn (BatchNormalization  (None, 17, 17, 528)  2112       ['block5a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5a_activation (Activation  (None, 17, 17, 528)  0          ['block5a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5a_se_squeeze (GlobalAver  (None, 528)         0           ['block5a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5a_se_reshape (Reshape)   (None, 1, 1, 528)    0           ['block5a_se_squeeze[0][0]']     
                                                                                                  
 block5a_se_reduce (Conv2D)     (None, 1, 1, 22)     11638       ['block5a_se_reshape[0][0]']     
                                                                                                  
 block5a_se_expand (Conv2D)     (None, 1, 1, 528)    12144       ['block5a_se_reduce[0][0]']      
                                                                                                  
 block5a_se_excite (Multiply)   (None, 17, 17, 528)  0           ['block5a_activation[0][0]',     
                                                                  'block5a_se_expand[0][0]']      
                                                                                                  
 block5a_project_conv (Conv2D)  (None, 17, 17, 120)  63360       ['block5a_se_excite[0][0]']      
                                                                                                  
 block5a_project_bn (BatchNorma  (None, 17, 17, 120)  480        ['block5a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5b_expand_conv (Conv2D)   (None, 17, 17, 720)  86400       ['block5a_project_bn[0][0]']     
                                                                                                  
 block5b_expand_bn (BatchNormal  (None, 17, 17, 720)  2880       ['block5b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5b_expand_activation (Act  (None, 17, 17, 720)  0          ['block5b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5b_dwconv (DepthwiseConv2  (None, 17, 17, 720)  18000      ['block5b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5b_bn (BatchNormalization  (None, 17, 17, 720)  2880       ['block5b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5b_activation (Activation  (None, 17, 17, 720)  0          ['block5b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5b_se_squeeze (GlobalAver  (None, 720)         0           ['block5b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5b_se_reshape (Reshape)   (None, 1, 1, 720)    0           ['block5b_se_squeeze[0][0]']     
                                                                                                  
 block5b_se_reduce (Conv2D)     (None, 1, 1, 30)     21630       ['block5b_se_reshape[0][0]']     
                                                                                                  
 block5b_se_expand (Conv2D)     (None, 1, 1, 720)    22320       ['block5b_se_reduce[0][0]']      
                                                                                                  
 block5b_se_excite (Multiply)   (None, 17, 17, 720)  0           ['block5b_activation[0][0]',     
                                                                  'block5b_se_expand[0][0]']      
                                                                                                  
 block5b_project_conv (Conv2D)  (None, 17, 17, 120)  86400       ['block5b_se_excite[0][0]']      
                                                                                                  
 block5b_project_bn (BatchNorma  (None, 17, 17, 120)  480        ['block5b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5b_drop (FixedDropout)    (None, 17, 17, 120)  0           ['block5b_project_bn[0][0]']     
                                                                                                  
 block5b_add (Add)              (None, 17, 17, 120)  0           ['block5b_drop[0][0]',           
                                                                  'block5a_project_bn[0][0]']     
                                                                                                  
 block5c_expand_conv (Conv2D)   (None, 17, 17, 720)  86400       ['block5b_add[0][0]']            
                                                                                                  
 block5c_expand_bn (BatchNormal  (None, 17, 17, 720)  2880       ['block5c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5c_expand_activation (Act  (None, 17, 17, 720)  0          ['block5c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5c_dwconv (DepthwiseConv2  (None, 17, 17, 720)  18000      ['block5c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5c_bn (BatchNormalization  (None, 17, 17, 720)  2880       ['block5c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5c_activation (Activation  (None, 17, 17, 720)  0          ['block5c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5c_se_squeeze (GlobalAver  (None, 720)         0           ['block5c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5c_se_reshape (Reshape)   (None, 1, 1, 720)    0           ['block5c_se_squeeze[0][0]']     
                                                                                                  
 block5c_se_reduce (Conv2D)     (None, 1, 1, 30)     21630       ['block5c_se_reshape[0][0]']     
                                                                                                  
 block5c_se_expand (Conv2D)     (None, 1, 1, 720)    22320       ['block5c_se_reduce[0][0]']      
                                                                                                  
 block5c_se_excite (Multiply)   (None, 17, 17, 720)  0           ['block5c_activation[0][0]',     
                                                                  'block5c_se_expand[0][0]']      
                                                                                                  
 block5c_project_conv (Conv2D)  (None, 17, 17, 120)  86400       ['block5c_se_excite[0][0]']      
                                                                                                  
 block5c_project_bn (BatchNorma  (None, 17, 17, 120)  480        ['block5c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5c_drop (FixedDropout)    (None, 17, 17, 120)  0           ['block5c_project_bn[0][0]']     
                                                                                                  
 block5c_add (Add)              (None, 17, 17, 120)  0           ['block5c_drop[0][0]',           
                                                                  'block5b_add[0][0]']            
                                                                                                  
 block5d_expand_conv (Conv2D)   (None, 17, 17, 720)  86400       ['block5c_add[0][0]']            
                                                                                                  
 block5d_expand_bn (BatchNormal  (None, 17, 17, 720)  2880       ['block5d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5d_expand_activation (Act  (None, 17, 17, 720)  0          ['block5d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5d_dwconv (DepthwiseConv2  (None, 17, 17, 720)  18000      ['block5d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5d_bn (BatchNormalization  (None, 17, 17, 720)  2880       ['block5d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5d_activation (Activation  (None, 17, 17, 720)  0          ['block5d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5d_se_squeeze (GlobalAver  (None, 720)         0           ['block5d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5d_se_reshape (Reshape)   (None, 1, 1, 720)    0           ['block5d_se_squeeze[0][0]']     
                                                                                                  
 block5d_se_reduce (Conv2D)     (None, 1, 1, 30)     21630       ['block5d_se_reshape[0][0]']     
                                                                                                  
 block5d_se_expand (Conv2D)     (None, 1, 1, 720)    22320       ['block5d_se_reduce[0][0]']      
                                                                                                  
 block5d_se_excite (Multiply)   (None, 17, 17, 720)  0           ['block5d_activation[0][0]',     
                                                                  'block5d_se_expand[0][0]']      
                                                                                                  
 block5d_project_conv (Conv2D)  (None, 17, 17, 120)  86400       ['block5d_se_excite[0][0]']      
                                                                                                  
 block5d_project_bn (BatchNorma  (None, 17, 17, 120)  480        ['block5d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5d_drop (FixedDropout)    (None, 17, 17, 120)  0           ['block5d_project_bn[0][0]']     
                                                                                                  
 block5d_add (Add)              (None, 17, 17, 120)  0           ['block5d_drop[0][0]',           
                                                                  'block5c_add[0][0]']            
                                                                                                  
 block6a_expand_conv (Conv2D)   (None, 17, 17, 720)  86400       ['block5d_add[0][0]']            
                                                                                                  
 block6a_expand_bn (BatchNormal  (None, 17, 17, 720)  2880       ['block6a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6a_expand_activation (Act  (None, 17, 17, 720)  0          ['block6a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6a_dwconv (DepthwiseConv2  (None, 9, 9, 720)   18000       ['block6a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6a_bn (BatchNormalization  (None, 9, 9, 720)   2880        ['block6a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6a_activation (Activation  (None, 9, 9, 720)   0           ['block6a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6a_se_squeeze (GlobalAver  (None, 720)         0           ['block6a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6a_se_reshape (Reshape)   (None, 1, 1, 720)    0           ['block6a_se_squeeze[0][0]']     
                                                                                                  
 block6a_se_reduce (Conv2D)     (None, 1, 1, 30)     21630       ['block6a_se_reshape[0][0]']     
                                                                                                  
 block6a_se_expand (Conv2D)     (None, 1, 1, 720)    22320       ['block6a_se_reduce[0][0]']      
                                                                                                  
 block6a_se_excite (Multiply)   (None, 9, 9, 720)    0           ['block6a_activation[0][0]',     
                                                                  'block6a_se_expand[0][0]']      
                                                                                                  
 block6a_project_conv (Conv2D)  (None, 9, 9, 208)    149760      ['block6a_se_excite[0][0]']      
                                                                                                  
 block6a_project_bn (BatchNorma  (None, 9, 9, 208)   832         ['block6a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6b_expand_conv (Conv2D)   (None, 9, 9, 1248)   259584      ['block6a_project_bn[0][0]']     
                                                                                                  
 block6b_expand_bn (BatchNormal  (None, 9, 9, 1248)  4992        ['block6b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6b_expand_activation (Act  (None, 9, 9, 1248)  0           ['block6b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6b_dwconv (DepthwiseConv2  (None, 9, 9, 1248)  31200       ['block6b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6b_bn (BatchNormalization  (None, 9, 9, 1248)  4992        ['block6b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6b_activation (Activation  (None, 9, 9, 1248)  0           ['block6b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6b_se_squeeze (GlobalAver  (None, 1248)        0           ['block6b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6b_se_reshape (Reshape)   (None, 1, 1, 1248)   0           ['block6b_se_squeeze[0][0]']     
                                                                                                  
 block6b_se_reduce (Conv2D)     (None, 1, 1, 52)     64948       ['block6b_se_reshape[0][0]']     
                                                                                                  
 block6b_se_expand (Conv2D)     (None, 1, 1, 1248)   66144       ['block6b_se_reduce[0][0]']      
                                                                                                  
 block6b_se_excite (Multiply)   (None, 9, 9, 1248)   0           ['block6b_activation[0][0]',     
                                                                  'block6b_se_expand[0][0]']      
                                                                                                  
 block6b_project_conv (Conv2D)  (None, 9, 9, 208)    259584      ['block6b_se_excite[0][0]']      
                                                                                                  
 block6b_project_bn (BatchNorma  (None, 9, 9, 208)   832         ['block6b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6b_drop (FixedDropout)    (None, 9, 9, 208)    0           ['block6b_project_bn[0][0]']     
                                                                                                  
 block6b_add (Add)              (None, 9, 9, 208)    0           ['block6b_drop[0][0]',           
                                                                  'block6a_project_bn[0][0]']     
                                                                                                  
 block6c_expand_conv (Conv2D)   (None, 9, 9, 1248)   259584      ['block6b_add[0][0]']            
                                                                                                  
 block6c_expand_bn (BatchNormal  (None, 9, 9, 1248)  4992        ['block6c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6c_expand_activation (Act  (None, 9, 9, 1248)  0           ['block6c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6c_dwconv (DepthwiseConv2  (None, 9, 9, 1248)  31200       ['block6c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6c_bn (BatchNormalization  (None, 9, 9, 1248)  4992        ['block6c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6c_activation (Activation  (None, 9, 9, 1248)  0           ['block6c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6c_se_squeeze (GlobalAver  (None, 1248)        0           ['block6c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6c_se_reshape (Reshape)   (None, 1, 1, 1248)   0           ['block6c_se_squeeze[0][0]']     
                                                                                                  
 block6c_se_reduce (Conv2D)     (None, 1, 1, 52)     64948       ['block6c_se_reshape[0][0]']     
                                                                                                  
 block6c_se_expand (Conv2D)     (None, 1, 1, 1248)   66144       ['block6c_se_reduce[0][0]']      
                                                                                                  
 block6c_se_excite (Multiply)   (None, 9, 9, 1248)   0           ['block6c_activation[0][0]',     
                                                                  'block6c_se_expand[0][0]']      
                                                                                                  
 block6c_project_conv (Conv2D)  (None, 9, 9, 208)    259584      ['block6c_se_excite[0][0]']      
                                                                                                  
 block6c_project_bn (BatchNorma  (None, 9, 9, 208)   832         ['block6c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6c_drop (FixedDropout)    (None, 9, 9, 208)    0           ['block6c_project_bn[0][0]']     
                                                                                                  
 block6c_add (Add)              (None, 9, 9, 208)    0           ['block6c_drop[0][0]',           
                                                                  'block6b_add[0][0]']            
                                                                                                  
 block6d_expand_conv (Conv2D)   (None, 9, 9, 1248)   259584      ['block6c_add[0][0]']            
                                                                                                  
 block6d_expand_bn (BatchNormal  (None, 9, 9, 1248)  4992        ['block6d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6d_expand_activation (Act  (None, 9, 9, 1248)  0           ['block6d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6d_dwconv (DepthwiseConv2  (None, 9, 9, 1248)  31200       ['block6d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6d_bn (BatchNormalization  (None, 9, 9, 1248)  4992        ['block6d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6d_activation (Activation  (None, 9, 9, 1248)  0           ['block6d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6d_se_squeeze (GlobalAver  (None, 1248)        0           ['block6d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6d_se_reshape (Reshape)   (None, 1, 1, 1248)   0           ['block6d_se_squeeze[0][0]']     
                                                                                                  
 block6d_se_reduce (Conv2D)     (None, 1, 1, 52)     64948       ['block6d_se_reshape[0][0]']     
                                                                                                  
 block6d_se_expand (Conv2D)     (None, 1, 1, 1248)   66144       ['block6d_se_reduce[0][0]']      
                                                                                                  
 block6d_se_excite (Multiply)   (None, 9, 9, 1248)   0           ['block6d_activation[0][0]',     
                                                                  'block6d_se_expand[0][0]']      
                                                                                                  
 block6d_project_conv (Conv2D)  (None, 9, 9, 208)    259584      ['block6d_se_excite[0][0]']      
                                                                                                  
 block6d_project_bn (BatchNorma  (None, 9, 9, 208)   832         ['block6d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6d_drop (FixedDropout)    (None, 9, 9, 208)    0           ['block6d_project_bn[0][0]']     
                                                                                                  
 block6d_add (Add)              (None, 9, 9, 208)    0           ['block6d_drop[0][0]',           
                                                                  'block6c_add[0][0]']            
                                                                                                  
 block6e_expand_conv (Conv2D)   (None, 9, 9, 1248)   259584      ['block6d_add[0][0]']            
                                                                                                  
 block6e_expand_bn (BatchNormal  (None, 9, 9, 1248)  4992        ['block6e_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6e_expand_activation (Act  (None, 9, 9, 1248)  0           ['block6e_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6e_dwconv (DepthwiseConv2  (None, 9, 9, 1248)  31200       ['block6e_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6e_bn (BatchNormalization  (None, 9, 9, 1248)  4992        ['block6e_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6e_activation (Activation  (None, 9, 9, 1248)  0           ['block6e_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6e_se_squeeze (GlobalAver  (None, 1248)        0           ['block6e_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6e_se_reshape (Reshape)   (None, 1, 1, 1248)   0           ['block6e_se_squeeze[0][0]']     
                                                                                                  
 block6e_se_reduce (Conv2D)     (None, 1, 1, 52)     64948       ['block6e_se_reshape[0][0]']     
                                                                                                  
 block6e_se_expand (Conv2D)     (None, 1, 1, 1248)   66144       ['block6e_se_reduce[0][0]']      
                                                                                                  
 block6e_se_excite (Multiply)   (None, 9, 9, 1248)   0           ['block6e_activation[0][0]',     
                                                                  'block6e_se_expand[0][0]']      
                                                                                                  
 block6e_project_conv (Conv2D)  (None, 9, 9, 208)    259584      ['block6e_se_excite[0][0]']      
                                                                                                  
 block6e_project_bn (BatchNorma  (None, 9, 9, 208)   832         ['block6e_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6e_drop (FixedDropout)    (None, 9, 9, 208)    0           ['block6e_project_bn[0][0]']     
                                                                                                  
 block6e_add (Add)              (None, 9, 9, 208)    0           ['block6e_drop[0][0]',           
                                                                  'block6d_add[0][0]']            
                                                                                                  
 block7a_expand_conv (Conv2D)   (None, 9, 9, 1248)   259584      ['block6e_add[0][0]']            
                                                                                                  
 block7a_expand_bn (BatchNormal  (None, 9, 9, 1248)  4992        ['block7a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block7a_expand_activation (Act  (None, 9, 9, 1248)  0           ['block7a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block7a_dwconv (DepthwiseConv2  (None, 9, 9, 1248)  11232       ['block7a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block7a_bn (BatchNormalization  (None, 9, 9, 1248)  4992        ['block7a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block7a_activation (Activation  (None, 9, 9, 1248)  0           ['block7a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block7a_se_squeeze (GlobalAver  (None, 1248)        0           ['block7a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block7a_se_reshape (Reshape)   (None, 1, 1, 1248)   0           ['block7a_se_squeeze[0][0]']     
                                                                                                  
 block7a_se_reduce (Conv2D)     (None, 1, 1, 52)     64948       ['block7a_se_reshape[0][0]']     
                                                                                                  
 block7a_se_expand (Conv2D)     (None, 1, 1, 1248)   66144       ['block7a_se_reduce[0][0]']      
                                                                                                  
 block7a_se_excite (Multiply)   (None, 9, 9, 1248)   0           ['block7a_activation[0][0]',     
                                                                  'block7a_se_expand[0][0]']      
                                                                                                  
 block7a_project_conv (Conv2D)  (None, 9, 9, 352)    439296      ['block7a_se_excite[0][0]']      
                                                                                                  
 block7a_project_bn (BatchNorma  (None, 9, 9, 352)   1408        ['block7a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block7b_expand_conv (Conv2D)   (None, 9, 9, 2112)   743424      ['block7a_project_bn[0][0]']     
                                                                                                  
 block7b_expand_bn (BatchNormal  (None, 9, 9, 2112)  8448        ['block7b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block7b_expand_activation (Act  (None, 9, 9, 2112)  0           ['block7b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block7b_dwconv (DepthwiseConv2  (None, 9, 9, 2112)  19008       ['block7b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block7b_bn (BatchNormalization  (None, 9, 9, 2112)  8448        ['block7b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block7b_activation (Activation  (None, 9, 9, 2112)  0           ['block7b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block7b_se_squeeze (GlobalAver  (None, 2112)        0           ['block7b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block7b_se_reshape (Reshape)   (None, 1, 1, 2112)   0           ['block7b_se_squeeze[0][0]']     
                                                                                                  
 block7b_se_reduce (Conv2D)     (None, 1, 1, 88)     185944      ['block7b_se_reshape[0][0]']     
                                                                                                  
 block7b_se_expand (Conv2D)     (None, 1, 1, 2112)   187968      ['block7b_se_reduce[0][0]']      
                                                                                                  
 block7b_se_excite (Multiply)   (None, 9, 9, 2112)   0           ['block7b_activation[0][0]',     
                                                                  'block7b_se_expand[0][0]']      
                                                                                                  
 block7b_project_conv (Conv2D)  (None, 9, 9, 352)    743424      ['block7b_se_excite[0][0]']      
                                                                                                  
 block7b_project_bn (BatchNorma  (None, 9, 9, 352)   1408        ['block7b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block7b_drop (FixedDropout)    (None, 9, 9, 352)    0           ['block7b_project_bn[0][0]']     
                                                                                                  
 block7b_add (Add)              (None, 9, 9, 352)    0           ['block7b_drop[0][0]',           
                                                                  'block7a_project_bn[0][0]']     
                                                                                                  
 top_conv (Conv2D)              (None, 9, 9, 1408)   495616      ['block7b_add[0][0]']            
                                                                                                  
 top_bn (BatchNormalization)    (None, 9, 9, 1408)   5632        ['top_conv[0][0]']               
                                                                                                  
 top_activation (Activation)    (None, 9, 9, 1408)   0           ['top_bn[0][0]']                 
                                                                                                  
 flatten_2 (Flatten)            (None, 114048)       0           ['top_activation[0][0]']         
                                                                                                  
 dense_4 (Dense)                (None, 1024)         116786176   ['flatten_2[0][0]']              
                                                                                                  
 dropout_2 (Dropout)            (None, 1024)         0           ['dense_4[0][0]']                
                                                                                                  
 dense_5 (Dense)                (None, 1)            1025        ['dropout_2[0][0]']              
                                                                                                  
==================================================================================================
Total params: 124,555,763
Trainable params: 116,787,201
Non-trainable params: 7,768,562
__________________________________________________________________________________________________

#get total parameters
model_params = model_final.count_params()

# Specify the optimizer, loss function and evaluation metrics. 
model_final.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001, weight_decay=1e-6), metrics=['accuracy'])

t1 = time.time()
#train the model
eff_history = model_final.fit_generator(train_generator, validation_data = validation_generator, steps_per_epoch = 100, epochs = 10)
fit_time = time.time() - t1

<ipython-input-66-b7f31b017b18>:3: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  eff_history = model_final.fit_generator(train_generator, validation_data = validation_generator, steps_per_epoch = 100, epochs = 10)

Epoch 1/10
100/100 [==============================] - 51s 419ms/step - loss: 0.4875 - accuracy: 0.9150 - val_loss: 0.0565 - val_accuracy: 0.9862
Epoch 2/10
100/100 [==============================] - 37s 366ms/step - loss: 0.2917 - accuracy: 0.9500 - val_loss: 0.0733 - val_accuracy: 0.9850
Epoch 3/10
100/100 [==============================] - 37s 365ms/step - loss: 0.2712 - accuracy: 0.9570 - val_loss: 0.0248 - val_accuracy: 0.9862
Epoch 4/10
100/100 [==============================] - 37s 366ms/step - loss: 0.2456 - accuracy: 0.9500 - val_loss: 0.0257 - val_accuracy: 0.9937
Epoch 5/10
100/100 [==============================] - 37s 365ms/step - loss: 0.2565 - accuracy: 0.9580 - val_loss: 0.0160 - val_accuracy: 0.9950
Epoch 6/10
100/100 [==============================] - 37s 366ms/step - loss: 0.2023 - accuracy: 0.9552 - val_loss: 0.0272 - val_accuracy: 0.9900
Epoch 7/10
100/100 [==============================] - 37s 367ms/step - loss: 0.1980 - accuracy: 0.9585 - val_loss: 0.0496 - val_accuracy: 0.9887
Epoch 8/10
100/100 [==============================] - 36s 363ms/step - loss: 0.1797 - accuracy: 0.9652 - val_loss: 0.0244 - val_accuracy: 0.9912
Epoch 9/10
100/100 [==============================] - 36s 362ms/step - loss: 0.2189 - accuracy: 0.9607 - val_loss: 0.0274 - val_accuracy: 0.9912
Epoch 10/10
100/100 [==============================] - 36s 363ms/step - loss: 0.2232 - accuracy: 0.9565 - val_loss: 0.0339 - val_accuracy: 0.9937

# time it took to fit the model
print(fit_time) 

380.2059442996979

#Plot training and validation accuracy and loss for each epoch
acc = eff_history.history['accuracy']
val_acc = eff_history.history['val_accuracy']
loss = eff_history.history['loss']
val_loss = eff_history.history['val_loss']

epochs = range(1,len(acc) + 1)

plt.plot(epochs,acc,label = 'Training Accuracy')
plt.plot(epochs,val_acc,label = 'Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()
plt.figure()

plt.plot(epochs,loss,label = 'Training loss')
plt.plot(epochs,val_loss,label = 'Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()

plt.show()

# Test dataset 
test_datagen = ImageDataGenerator(rescale=1./255)

test_generator = test_datagen.flow_from_directory(
        test_dir,
        target_size=(260, 260),
        shuffle = False,
        class_mode='binary',
        batch_size=1)

Found 2023 images belonging to 2 classes.

#Get test length
filenames = test_generator.filenames
nb_samples = len(filenames)

#Predict on test set
predict = model_final.predict_generator(test_generator,steps = nb_samples)

<ipython-input-71-7710eff794cf>:2: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.
  predict = model_final.predict_generator(test_generator,steps = nb_samples)

#Get list of prediction results
pred_list = []
for i in predict:
    if i > 0.5:
        result = 1 #dog
        pred_list.append(result)
    else:
      result = 0 #cat
      pred_list.append(result)

#Create dataframe of image ID, image true label, image predicted label
import pandas as pd
image_ids = [name.split('/')[-1] for name in test_generator.filenames]
image_label = [name.split('/')[0] for name in test_generator.filenames]
data = {'id': image_ids, 'label':image_label, 'prediction':pred_list} 
data_df = pd.DataFrame(data)
data_df.label.replace(('cats', 'dogs'), (0, 1), inplace=True) # change cat and dog label to 0 or 1

#Get test accuracy score
from sklearn.metrics import accuracy_score, confusion_matrix
test_accuracy = accuracy_score(data_df['label'], data_df['prediction'])
print('Test Accuracy: ', round((test_accuracy * 100), 2), "%")

Test Accuracy:  98.32 %

from sklearn.metrics import classification_report
#Classification Report
print(classification_report(data_df['label'], data_df['prediction']))

              precision    recall  f1-score   support

           0       0.99      0.97      0.98      1011
           1       0.97      0.99      0.98      1012

    accuracy                           0.98      2023
   macro avg       0.98      0.98      0.98      2023
weighted avg       0.98      0.98      0.98      2023

#Create confusion matrix
import seaborn as sns
label = [0, 1] #0 = cat and 1 = dog
cm = confusion_matrix(data_df['label'], data_df['prediction'], labels = label)
#Plot
ax= plt.subplot()
sns.heatmap(cm, annot=True, fmt='g', ax=ax);
# labels, title and ticks
ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); 
ax.set_title('Confusion Matrix'); 
ax.xaxis.set_ticklabels(["Cat", "Dog"]); ax.yaxis.set_ticklabels(["Cat", "Dog"])

[Text(0, 0.5, 'Cat'), Text(0, 1.5, 'Dog')]

ExperimentLog.loc[len(ExperimentLog)] = [
    "EfficientNet B2",
    260,
    "RMSprop", 
    10, 
    max(acc), 
    max(val_acc),
    test_accuracy,
    fit_time,
    model_params
]

ExperimentLog 

	Base Model	Input Resolution	Optimizer	Epochs	Training Accuracy	Validation Accuracy	Test Accuracy	Fit Time	Total Parameters
0	EfficientNet B0	224	RMSprop	10	0.951500	0.98750	0.971330	2195.619411	68276893
1	EfficientNet B0 with decay	224	RMSprop	10	0.956171	0.98750	0.974790	381.655278	68276893
2	EfficientNet B1	240	RMSprop	10	0.959698	0.99125	0.985171	349.486195	90463361
3	EfficientNet B2	260	RMSprop	10	0.965239	0.99500	0.983193	380.205944	124555763

EfficientNetB3

# Add rescaling and augmentation to ImageDataGenerator for the training set
train_datagen = ImageDataGenerator(rescale = 1./255., rotation_range = 40, width_shift_range = 0.2, height_shift_range = 0.2, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True, validation_split=0.1) # set validation split
# Rescale validation set. No augmentation on the validation set. 
validation_datagen = ImageDataGenerator(rescale = 1./255.,validation_split=0.1) # set validation split
#Read images directly from directory. 
train_generator = train_datagen.flow_from_directory(train_dir, seed = 42, shuffle = True, batch_size = 20, class_mode = 'binary', target_size = (300, 300), subset='training') #set as training data
validation_generator = validation_datagen.flow_from_directory(train_dir, seed = 42, shuffle = True, batch_size = 20, class_mode = 'binary', target_size = (300, 300), subset='validation') # same directory as training data. Set as validation data

Found 7205 images belonging to 2 classes.
Found 800 images belonging to 2 classes.

#Instantiates the EfficientNet architecture
base_model = efn.EfficientNetB3(input_shape = (300, 300, 3), include_top = False, weights = 'imagenet')

Downloading data from https://github.com/Callidior/keras-applications/releases/download/efficientnet/efficientnet-b3_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5
44107200/44107200 [==============================] - 3s 0us/step

# Set trainable attribute to false for all of the base model layers
for layer in base_model.layers:
    layer.trainable = False

#Build on top of existing base model. 
x = base_model.output
x = layers.Flatten()(x) #convert to 1D array
x = layers.Dense(1024, activation="relu")(x) #fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dropout(0.5)(x) #Drops 50% of inputs to zero at each training iteration (prevents overfitting)

# Add a final sigmoid layer with 1 node for classification output (probability between 0 and 1)
predictions = layers.Dense(1, activation="sigmoid")(x)
model_final = Model(inputs = base_model.input, outputs = predictions)

#Print model summary
from torchsummary import summary
model_sum = model_final.summary()

Model: "model_3"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_4 (InputLayer)           [(None, 300, 300, 3  0           []                               
                                )]                                                                
                                                                                                  
 stem_conv (Conv2D)             (None, 150, 150, 40  1080        ['input_4[0][0]']                
                                )                                                                 
                                                                                                  
 stem_bn (BatchNormalization)   (None, 150, 150, 40  160         ['stem_conv[0][0]']              
                                )                                                                 
                                                                                                  
 stem_activation (Activation)   (None, 150, 150, 40  0           ['stem_bn[0][0]']                
                                )                                                                 
                                                                                                  
 block1a_dwconv (DepthwiseConv2  (None, 150, 150, 40  360        ['stem_activation[0][0]']        
 D)                             )                                                                 
                                                                                                  
 block1a_bn (BatchNormalization  (None, 150, 150, 40  160        ['block1a_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block1a_activation (Activation  (None, 150, 150, 40  0          ['block1a_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block1a_se_squeeze (GlobalAver  (None, 40)          0           ['block1a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block1a_se_reshape (Reshape)   (None, 1, 1, 40)     0           ['block1a_se_squeeze[0][0]']     
                                                                                                  
 block1a_se_reduce (Conv2D)     (None, 1, 1, 10)     410         ['block1a_se_reshape[0][0]']     
                                                                                                  
 block1a_se_expand (Conv2D)     (None, 1, 1, 40)     440         ['block1a_se_reduce[0][0]']      
                                                                                                  
 block1a_se_excite (Multiply)   (None, 150, 150, 40  0           ['block1a_activation[0][0]',     
                                )                                 'block1a_se_expand[0][0]']      
                                                                                                  
 block1a_project_conv (Conv2D)  (None, 150, 150, 24  960         ['block1a_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block1a_project_bn (BatchNorma  (None, 150, 150, 24  96         ['block1a_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block1b_dwconv (DepthwiseConv2  (None, 150, 150, 24  216        ['block1a_project_bn[0][0]']     
 D)                             )                                                                 
                                                                                                  
 block1b_bn (BatchNormalization  (None, 150, 150, 24  96         ['block1b_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block1b_activation (Activation  (None, 150, 150, 24  0          ['block1b_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block1b_se_squeeze (GlobalAver  (None, 24)          0           ['block1b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block1b_se_reshape (Reshape)   (None, 1, 1, 24)     0           ['block1b_se_squeeze[0][0]']     
                                                                                                  
 block1b_se_reduce (Conv2D)     (None, 1, 1, 6)      150         ['block1b_se_reshape[0][0]']     
                                                                                                  
 block1b_se_expand (Conv2D)     (None, 1, 1, 24)     168         ['block1b_se_reduce[0][0]']      
                                                                                                  
 block1b_se_excite (Multiply)   (None, 150, 150, 24  0           ['block1b_activation[0][0]',     
                                )                                 'block1b_se_expand[0][0]']      
                                                                                                  
 block1b_project_conv (Conv2D)  (None, 150, 150, 24  576         ['block1b_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block1b_project_bn (BatchNorma  (None, 150, 150, 24  96         ['block1b_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block1b_drop (FixedDropout)    (None, 150, 150, 24  0           ['block1b_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block1b_add (Add)              (None, 150, 150, 24  0           ['block1b_drop[0][0]',           
                                )                                 'block1a_project_bn[0][0]']     
                                                                                                  
 block2a_expand_conv (Conv2D)   (None, 150, 150, 14  3456        ['block1b_add[0][0]']            
                                4)                                                                
                                                                                                  
 block2a_expand_bn (BatchNormal  (None, 150, 150, 14  576        ['block2a_expand_conv[0][0]']    
 ization)                       4)                                                                
                                                                                                  
 block2a_expand_activation (Act  (None, 150, 150, 14  0          ['block2a_expand_bn[0][0]']      
 ivation)                       4)                                                                
                                                                                                  
 block2a_dwconv (DepthwiseConv2  (None, 75, 75, 144)  1296       ['block2a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2a_bn (BatchNormalization  (None, 75, 75, 144)  576        ['block2a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2a_activation (Activation  (None, 75, 75, 144)  0          ['block2a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2a_se_squeeze (GlobalAver  (None, 144)         0           ['block2a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2a_se_squeeze[0][0]']     
                                                                                                  
 block2a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2a_se_reshape[0][0]']     
                                                                                                  
 block2a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2a_se_reduce[0][0]']      
                                                                                                  
 block2a_se_excite (Multiply)   (None, 75, 75, 144)  0           ['block2a_activation[0][0]',     
                                                                  'block2a_se_expand[0][0]']      
                                                                                                  
 block2a_project_conv (Conv2D)  (None, 75, 75, 32)   4608        ['block2a_se_excite[0][0]']      
                                                                                                  
 block2a_project_bn (BatchNorma  (None, 75, 75, 32)  128         ['block2a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2b_expand_conv (Conv2D)   (None, 75, 75, 192)  6144        ['block2a_project_bn[0][0]']     
                                                                                                  
 block2b_expand_bn (BatchNormal  (None, 75, 75, 192)  768        ['block2b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block2b_expand_activation (Act  (None, 75, 75, 192)  0          ['block2b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block2b_dwconv (DepthwiseConv2  (None, 75, 75, 192)  1728       ['block2b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2b_bn (BatchNormalization  (None, 75, 75, 192)  768        ['block2b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2b_activation (Activation  (None, 75, 75, 192)  0          ['block2b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2b_se_squeeze (GlobalAver  (None, 192)         0           ['block2b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2b_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2b_se_squeeze[0][0]']     
                                                                                                  
 block2b_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2b_se_reshape[0][0]']     
                                                                                                  
 block2b_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2b_se_reduce[0][0]']      
                                                                                                  
 block2b_se_excite (Multiply)   (None, 75, 75, 192)  0           ['block2b_activation[0][0]',     
                                                                  'block2b_se_expand[0][0]']      
                                                                                                  
 block2b_project_conv (Conv2D)  (None, 75, 75, 32)   6144        ['block2b_se_excite[0][0]']      
                                                                                                  
 block2b_project_bn (BatchNorma  (None, 75, 75, 32)  128         ['block2b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2b_drop (FixedDropout)    (None, 75, 75, 32)   0           ['block2b_project_bn[0][0]']     
                                                                                                  
 block2b_add (Add)              (None, 75, 75, 32)   0           ['block2b_drop[0][0]',           
                                                                  'block2a_project_bn[0][0]']     
                                                                                                  
 block2c_expand_conv (Conv2D)   (None, 75, 75, 192)  6144        ['block2b_add[0][0]']            
                                                                                                  
 block2c_expand_bn (BatchNormal  (None, 75, 75, 192)  768        ['block2c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block2c_expand_activation (Act  (None, 75, 75, 192)  0          ['block2c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block2c_dwconv (DepthwiseConv2  (None, 75, 75, 192)  1728       ['block2c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2c_bn (BatchNormalization  (None, 75, 75, 192)  768        ['block2c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2c_activation (Activation  (None, 75, 75, 192)  0          ['block2c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2c_se_squeeze (GlobalAver  (None, 192)         0           ['block2c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2c_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2c_se_squeeze[0][0]']     
                                                                                                  
 block2c_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2c_se_reshape[0][0]']     
                                                                                                  
 block2c_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2c_se_reduce[0][0]']      
                                                                                                  
 block2c_se_excite (Multiply)   (None, 75, 75, 192)  0           ['block2c_activation[0][0]',     
                                                                  'block2c_se_expand[0][0]']      
                                                                                                  
 block2c_project_conv (Conv2D)  (None, 75, 75, 32)   6144        ['block2c_se_excite[0][0]']      
                                                                                                  
 block2c_project_bn (BatchNorma  (None, 75, 75, 32)  128         ['block2c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2c_drop (FixedDropout)    (None, 75, 75, 32)   0           ['block2c_project_bn[0][0]']     
                                                                                                  
 block2c_add (Add)              (None, 75, 75, 32)   0           ['block2c_drop[0][0]',           
                                                                  'block2b_add[0][0]']            
                                                                                                  
 block3a_expand_conv (Conv2D)   (None, 75, 75, 192)  6144        ['block2c_add[0][0]']            
                                                                                                  
 block3a_expand_bn (BatchNormal  (None, 75, 75, 192)  768        ['block3a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3a_expand_activation (Act  (None, 75, 75, 192)  0          ['block3a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3a_dwconv (DepthwiseConv2  (None, 38, 38, 192)  4800       ['block3a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3a_bn (BatchNormalization  (None, 38, 38, 192)  768        ['block3a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3a_activation (Activation  (None, 38, 38, 192)  0          ['block3a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3a_se_squeeze (GlobalAver  (None, 192)         0           ['block3a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3a_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block3a_se_squeeze[0][0]']     
                                                                                                  
 block3a_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block3a_se_reshape[0][0]']     
                                                                                                  
 block3a_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block3a_se_reduce[0][0]']      
                                                                                                  
 block3a_se_excite (Multiply)   (None, 38, 38, 192)  0           ['block3a_activation[0][0]',     
                                                                  'block3a_se_expand[0][0]']      
                                                                                                  
 block3a_project_conv (Conv2D)  (None, 38, 38, 48)   9216        ['block3a_se_excite[0][0]']      
                                                                                                  
 block3a_project_bn (BatchNorma  (None, 38, 38, 48)  192         ['block3a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3b_expand_conv (Conv2D)   (None, 38, 38, 288)  13824       ['block3a_project_bn[0][0]']     
                                                                                                  
 block3b_expand_bn (BatchNormal  (None, 38, 38, 288)  1152       ['block3b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3b_expand_activation (Act  (None, 38, 38, 288)  0          ['block3b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3b_dwconv (DepthwiseConv2  (None, 38, 38, 288)  7200       ['block3b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3b_bn (BatchNormalization  (None, 38, 38, 288)  1152       ['block3b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3b_activation (Activation  (None, 38, 38, 288)  0          ['block3b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3b_se_squeeze (GlobalAver  (None, 288)         0           ['block3b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3b_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block3b_se_squeeze[0][0]']     
                                                                                                  
 block3b_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block3b_se_reshape[0][0]']     
                                                                                                  
 block3b_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block3b_se_reduce[0][0]']      
                                                                                                  
 block3b_se_excite (Multiply)   (None, 38, 38, 288)  0           ['block3b_activation[0][0]',     
                                                                  'block3b_se_expand[0][0]']      
                                                                                                  
 block3b_project_conv (Conv2D)  (None, 38, 38, 48)   13824       ['block3b_se_excite[0][0]']      
                                                                                                  
 block3b_project_bn (BatchNorma  (None, 38, 38, 48)  192         ['block3b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3b_drop (FixedDropout)    (None, 38, 38, 48)   0           ['block3b_project_bn[0][0]']     
                                                                                                  
 block3b_add (Add)              (None, 38, 38, 48)   0           ['block3b_drop[0][0]',           
                                                                  'block3a_project_bn[0][0]']     
                                                                                                  
 block3c_expand_conv (Conv2D)   (None, 38, 38, 288)  13824       ['block3b_add[0][0]']            
                                                                                                  
 block3c_expand_bn (BatchNormal  (None, 38, 38, 288)  1152       ['block3c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3c_expand_activation (Act  (None, 38, 38, 288)  0          ['block3c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3c_dwconv (DepthwiseConv2  (None, 38, 38, 288)  7200       ['block3c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3c_bn (BatchNormalization  (None, 38, 38, 288)  1152       ['block3c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3c_activation (Activation  (None, 38, 38, 288)  0          ['block3c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3c_se_squeeze (GlobalAver  (None, 288)         0           ['block3c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3c_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block3c_se_squeeze[0][0]']     
                                                                                                  
 block3c_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block3c_se_reshape[0][0]']     
                                                                                                  
 block3c_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block3c_se_reduce[0][0]']      
                                                                                                  
 block3c_se_excite (Multiply)   (None, 38, 38, 288)  0           ['block3c_activation[0][0]',     
                                                                  'block3c_se_expand[0][0]']      
                                                                                                  
 block3c_project_conv (Conv2D)  (None, 38, 38, 48)   13824       ['block3c_se_excite[0][0]']      
                                                                                                  
 block3c_project_bn (BatchNorma  (None, 38, 38, 48)  192         ['block3c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3c_drop (FixedDropout)    (None, 38, 38, 48)   0           ['block3c_project_bn[0][0]']     
                                                                                                  
 block3c_add (Add)              (None, 38, 38, 48)   0           ['block3c_drop[0][0]',           
                                                                  'block3b_add[0][0]']            
                                                                                                  
 block4a_expand_conv (Conv2D)   (None, 38, 38, 288)  13824       ['block3c_add[0][0]']            
                                                                                                  
 block4a_expand_bn (BatchNormal  (None, 38, 38, 288)  1152       ['block4a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4a_expand_activation (Act  (None, 38, 38, 288)  0          ['block4a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4a_dwconv (DepthwiseConv2  (None, 19, 19, 288)  2592       ['block4a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4a_bn (BatchNormalization  (None, 19, 19, 288)  1152       ['block4a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4a_activation (Activation  (None, 19, 19, 288)  0          ['block4a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4a_se_squeeze (GlobalAver  (None, 288)         0           ['block4a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4a_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block4a_se_squeeze[0][0]']     
                                                                                                  
 block4a_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block4a_se_reshape[0][0]']     
                                                                                                  
 block4a_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block4a_se_reduce[0][0]']      
                                                                                                  
 block4a_se_excite (Multiply)   (None, 19, 19, 288)  0           ['block4a_activation[0][0]',     
                                                                  'block4a_se_expand[0][0]']      
                                                                                                  
 block4a_project_conv (Conv2D)  (None, 19, 19, 96)   27648       ['block4a_se_excite[0][0]']      
                                                                                                  
 block4a_project_bn (BatchNorma  (None, 19, 19, 96)  384         ['block4a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4b_expand_conv (Conv2D)   (None, 19, 19, 576)  55296       ['block4a_project_bn[0][0]']     
                                                                                                  
 block4b_expand_bn (BatchNormal  (None, 19, 19, 576)  2304       ['block4b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4b_expand_activation (Act  (None, 19, 19, 576)  0          ['block4b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4b_dwconv (DepthwiseConv2  (None, 19, 19, 576)  5184       ['block4b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4b_bn (BatchNormalization  (None, 19, 19, 576)  2304       ['block4b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4b_activation (Activation  (None, 19, 19, 576)  0          ['block4b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4b_se_squeeze (GlobalAver  (None, 576)         0           ['block4b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4b_se_reshape (Reshape)   (None, 1, 1, 576)    0           ['block4b_se_squeeze[0][0]']     
                                                                                                  
 block4b_se_reduce (Conv2D)     (None, 1, 1, 24)     13848       ['block4b_se_reshape[0][0]']     
                                                                                                  
 block4b_se_expand (Conv2D)     (None, 1, 1, 576)    14400       ['block4b_se_reduce[0][0]']      
                                                                                                  
 block4b_se_excite (Multiply)   (None, 19, 19, 576)  0           ['block4b_activation[0][0]',     
                                                                  'block4b_se_expand[0][0]']      
                                                                                                  
 block4b_project_conv (Conv2D)  (None, 19, 19, 96)   55296       ['block4b_se_excite[0][0]']      
                                                                                                  
 block4b_project_bn (BatchNorma  (None, 19, 19, 96)  384         ['block4b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4b_drop (FixedDropout)    (None, 19, 19, 96)   0           ['block4b_project_bn[0][0]']     
                                                                                                  
 block4b_add (Add)              (None, 19, 19, 96)   0           ['block4b_drop[0][0]',           
                                                                  'block4a_project_bn[0][0]']     
                                                                                                  
 block4c_expand_conv (Conv2D)   (None, 19, 19, 576)  55296       ['block4b_add[0][0]']            
                                                                                                  
 block4c_expand_bn (BatchNormal  (None, 19, 19, 576)  2304       ['block4c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4c_expand_activation (Act  (None, 19, 19, 576)  0          ['block4c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4c_dwconv (DepthwiseConv2  (None, 19, 19, 576)  5184       ['block4c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4c_bn (BatchNormalization  (None, 19, 19, 576)  2304       ['block4c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4c_activation (Activation  (None, 19, 19, 576)  0          ['block4c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4c_se_squeeze (GlobalAver  (None, 576)         0           ['block4c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4c_se_reshape (Reshape)   (None, 1, 1, 576)    0           ['block4c_se_squeeze[0][0]']     
                                                                                                  
 block4c_se_reduce (Conv2D)     (None, 1, 1, 24)     13848       ['block4c_se_reshape[0][0]']     
                                                                                                  
 block4c_se_expand (Conv2D)     (None, 1, 1, 576)    14400       ['block4c_se_reduce[0][0]']      
                                                                                                  
 block4c_se_excite (Multiply)   (None, 19, 19, 576)  0           ['block4c_activation[0][0]',     
                                                                  'block4c_se_expand[0][0]']      
                                                                                                  
 block4c_project_conv (Conv2D)  (None, 19, 19, 96)   55296       ['block4c_se_excite[0][0]']      
                                                                                                  
 block4c_project_bn (BatchNorma  (None, 19, 19, 96)  384         ['block4c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4c_drop (FixedDropout)    (None, 19, 19, 96)   0           ['block4c_project_bn[0][0]']     
                                                                                                  
 block4c_add (Add)              (None, 19, 19, 96)   0           ['block4c_drop[0][0]',           
                                                                  'block4b_add[0][0]']            
                                                                                                  
 block4d_expand_conv (Conv2D)   (None, 19, 19, 576)  55296       ['block4c_add[0][0]']            
                                                                                                  
 block4d_expand_bn (BatchNormal  (None, 19, 19, 576)  2304       ['block4d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4d_expand_activation (Act  (None, 19, 19, 576)  0          ['block4d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4d_dwconv (DepthwiseConv2  (None, 19, 19, 576)  5184       ['block4d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4d_bn (BatchNormalization  (None, 19, 19, 576)  2304       ['block4d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4d_activation (Activation  (None, 19, 19, 576)  0          ['block4d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4d_se_squeeze (GlobalAver  (None, 576)         0           ['block4d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4d_se_reshape (Reshape)   (None, 1, 1, 576)    0           ['block4d_se_squeeze[0][0]']     
                                                                                                  
 block4d_se_reduce (Conv2D)     (None, 1, 1, 24)     13848       ['block4d_se_reshape[0][0]']     
                                                                                                  
 block4d_se_expand (Conv2D)     (None, 1, 1, 576)    14400       ['block4d_se_reduce[0][0]']      
                                                                                                  
 block4d_se_excite (Multiply)   (None, 19, 19, 576)  0           ['block4d_activation[0][0]',     
                                                                  'block4d_se_expand[0][0]']      
                                                                                                  
 block4d_project_conv (Conv2D)  (None, 19, 19, 96)   55296       ['block4d_se_excite[0][0]']      
                                                                                                  
 block4d_project_bn (BatchNorma  (None, 19, 19, 96)  384         ['block4d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4d_drop (FixedDropout)    (None, 19, 19, 96)   0           ['block4d_project_bn[0][0]']     
                                                                                                  
 block4d_add (Add)              (None, 19, 19, 96)   0           ['block4d_drop[0][0]',           
                                                                  'block4c_add[0][0]']            
                                                                                                  
 block4e_expand_conv (Conv2D)   (None, 19, 19, 576)  55296       ['block4d_add[0][0]']            
                                                                                                  
 block4e_expand_bn (BatchNormal  (None, 19, 19, 576)  2304       ['block4e_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4e_expand_activation (Act  (None, 19, 19, 576)  0          ['block4e_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4e_dwconv (DepthwiseConv2  (None, 19, 19, 576)  5184       ['block4e_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4e_bn (BatchNormalization  (None, 19, 19, 576)  2304       ['block4e_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4e_activation (Activation  (None, 19, 19, 576)  0          ['block4e_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4e_se_squeeze (GlobalAver  (None, 576)         0           ['block4e_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4e_se_reshape (Reshape)   (None, 1, 1, 576)    0           ['block4e_se_squeeze[0][0]']     
                                                                                                  
 block4e_se_reduce (Conv2D)     (None, 1, 1, 24)     13848       ['block4e_se_reshape[0][0]']     
                                                                                                  
 block4e_se_expand (Conv2D)     (None, 1, 1, 576)    14400       ['block4e_se_reduce[0][0]']      
                                                                                                  
 block4e_se_excite (Multiply)   (None, 19, 19, 576)  0           ['block4e_activation[0][0]',     
                                                                  'block4e_se_expand[0][0]']      
                                                                                                  
 block4e_project_conv (Conv2D)  (None, 19, 19, 96)   55296       ['block4e_se_excite[0][0]']      
                                                                                                  
 block4e_project_bn (BatchNorma  (None, 19, 19, 96)  384         ['block4e_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4e_drop (FixedDropout)    (None, 19, 19, 96)   0           ['block4e_project_bn[0][0]']     
                                                                                                  
 block4e_add (Add)              (None, 19, 19, 96)   0           ['block4e_drop[0][0]',           
                                                                  'block4d_add[0][0]']            
                                                                                                  
 block5a_expand_conv (Conv2D)   (None, 19, 19, 576)  55296       ['block4e_add[0][0]']            
                                                                                                  
 block5a_expand_bn (BatchNormal  (None, 19, 19, 576)  2304       ['block5a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5a_expand_activation (Act  (None, 19, 19, 576)  0          ['block5a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5a_dwconv (DepthwiseConv2  (None, 19, 19, 576)  14400      ['block5a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5a_bn (BatchNormalization  (None, 19, 19, 576)  2304       ['block5a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5a_activation (Activation  (None, 19, 19, 576)  0          ['block5a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5a_se_squeeze (GlobalAver  (None, 576)         0           ['block5a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5a_se_reshape (Reshape)   (None, 1, 1, 576)    0           ['block5a_se_squeeze[0][0]']     
                                                                                                  
 block5a_se_reduce (Conv2D)     (None, 1, 1, 24)     13848       ['block5a_se_reshape[0][0]']     
                                                                                                  
 block5a_se_expand (Conv2D)     (None, 1, 1, 576)    14400       ['block5a_se_reduce[0][0]']      
                                                                                                  
 block5a_se_excite (Multiply)   (None, 19, 19, 576)  0           ['block5a_activation[0][0]',     
                                                                  'block5a_se_expand[0][0]']      
                                                                                                  
 block5a_project_conv (Conv2D)  (None, 19, 19, 136)  78336       ['block5a_se_excite[0][0]']      
                                                                                                  
 block5a_project_bn (BatchNorma  (None, 19, 19, 136)  544        ['block5a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5b_expand_conv (Conv2D)   (None, 19, 19, 816)  110976      ['block5a_project_bn[0][0]']     
                                                                                                  
 block5b_expand_bn (BatchNormal  (None, 19, 19, 816)  3264       ['block5b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5b_expand_activation (Act  (None, 19, 19, 816)  0          ['block5b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5b_dwconv (DepthwiseConv2  (None, 19, 19, 816)  20400      ['block5b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5b_bn (BatchNormalization  (None, 19, 19, 816)  3264       ['block5b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5b_activation (Activation  (None, 19, 19, 816)  0          ['block5b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5b_se_squeeze (GlobalAver  (None, 816)         0           ['block5b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5b_se_reshape (Reshape)   (None, 1, 1, 816)    0           ['block5b_se_squeeze[0][0]']     
                                                                                                  
 block5b_se_reduce (Conv2D)     (None, 1, 1, 34)     27778       ['block5b_se_reshape[0][0]']     
                                                                                                  
 block5b_se_expand (Conv2D)     (None, 1, 1, 816)    28560       ['block5b_se_reduce[0][0]']      
                                                                                                  
 block5b_se_excite (Multiply)   (None, 19, 19, 816)  0           ['block5b_activation[0][0]',     
                                                                  'block5b_se_expand[0][0]']      
                                                                                                  
 block5b_project_conv (Conv2D)  (None, 19, 19, 136)  110976      ['block5b_se_excite[0][0]']      
                                                                                                  
 block5b_project_bn (BatchNorma  (None, 19, 19, 136)  544        ['block5b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5b_drop (FixedDropout)    (None, 19, 19, 136)  0           ['block5b_project_bn[0][0]']     
                                                                                                  
 block5b_add (Add)              (None, 19, 19, 136)  0           ['block5b_drop[0][0]',           
                                                                  'block5a_project_bn[0][0]']     
                                                                                                  
 block5c_expand_conv (Conv2D)   (None, 19, 19, 816)  110976      ['block5b_add[0][0]']            
                                                                                                  
 block5c_expand_bn (BatchNormal  (None, 19, 19, 816)  3264       ['block5c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5c_expand_activation (Act  (None, 19, 19, 816)  0          ['block5c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5c_dwconv (DepthwiseConv2  (None, 19, 19, 816)  20400      ['block5c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5c_bn (BatchNormalization  (None, 19, 19, 816)  3264       ['block5c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5c_activation (Activation  (None, 19, 19, 816)  0          ['block5c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5c_se_squeeze (GlobalAver  (None, 816)         0           ['block5c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5c_se_reshape (Reshape)   (None, 1, 1, 816)    0           ['block5c_se_squeeze[0][0]']     
                                                                                                  
 block5c_se_reduce (Conv2D)     (None, 1, 1, 34)     27778       ['block5c_se_reshape[0][0]']     
                                                                                                  
 block5c_se_expand (Conv2D)     (None, 1, 1, 816)    28560       ['block5c_se_reduce[0][0]']      
                                                                                                  
 block5c_se_excite (Multiply)   (None, 19, 19, 816)  0           ['block5c_activation[0][0]',     
                                                                  'block5c_se_expand[0][0]']      
                                                                                                  
 block5c_project_conv (Conv2D)  (None, 19, 19, 136)  110976      ['block5c_se_excite[0][0]']      
                                                                                                  
 block5c_project_bn (BatchNorma  (None, 19, 19, 136)  544        ['block5c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5c_drop (FixedDropout)    (None, 19, 19, 136)  0           ['block5c_project_bn[0][0]']     
                                                                                                  
 block5c_add (Add)              (None, 19, 19, 136)  0           ['block5c_drop[0][0]',           
                                                                  'block5b_add[0][0]']            
                                                                                                  
 block5d_expand_conv (Conv2D)   (None, 19, 19, 816)  110976      ['block5c_add[0][0]']            
                                                                                                  
 block5d_expand_bn (BatchNormal  (None, 19, 19, 816)  3264       ['block5d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5d_expand_activation (Act  (None, 19, 19, 816)  0          ['block5d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5d_dwconv (DepthwiseConv2  (None, 19, 19, 816)  20400      ['block5d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5d_bn (BatchNormalization  (None, 19, 19, 816)  3264       ['block5d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5d_activation (Activation  (None, 19, 19, 816)  0          ['block5d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5d_se_squeeze (GlobalAver  (None, 816)         0           ['block5d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5d_se_reshape (Reshape)   (None, 1, 1, 816)    0           ['block5d_se_squeeze[0][0]']     
                                                                                                  
 block5d_se_reduce (Conv2D)     (None, 1, 1, 34)     27778       ['block5d_se_reshape[0][0]']     
                                                                                                  
 block5d_se_expand (Conv2D)     (None, 1, 1, 816)    28560       ['block5d_se_reduce[0][0]']      
                                                                                                  
 block5d_se_excite (Multiply)   (None, 19, 19, 816)  0           ['block5d_activation[0][0]',     
                                                                  'block5d_se_expand[0][0]']      
                                                                                                  
 block5d_project_conv (Conv2D)  (None, 19, 19, 136)  110976      ['block5d_se_excite[0][0]']      
                                                                                                  
 block5d_project_bn (BatchNorma  (None, 19, 19, 136)  544        ['block5d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5d_drop (FixedDropout)    (None, 19, 19, 136)  0           ['block5d_project_bn[0][0]']     
                                                                                                  
 block5d_add (Add)              (None, 19, 19, 136)  0           ['block5d_drop[0][0]',           
                                                                  'block5c_add[0][0]']            
                                                                                                  
 block5e_expand_conv (Conv2D)   (None, 19, 19, 816)  110976      ['block5d_add[0][0]']            
                                                                                                  
 block5e_expand_bn (BatchNormal  (None, 19, 19, 816)  3264       ['block5e_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5e_expand_activation (Act  (None, 19, 19, 816)  0          ['block5e_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5e_dwconv (DepthwiseConv2  (None, 19, 19, 816)  20400      ['block5e_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5e_bn (BatchNormalization  (None, 19, 19, 816)  3264       ['block5e_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5e_activation (Activation  (None, 19, 19, 816)  0          ['block5e_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5e_se_squeeze (GlobalAver  (None, 816)         0           ['block5e_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5e_se_reshape (Reshape)   (None, 1, 1, 816)    0           ['block5e_se_squeeze[0][0]']     
                                                                                                  
 block5e_se_reduce (Conv2D)     (None, 1, 1, 34)     27778       ['block5e_se_reshape[0][0]']     
                                                                                                  
 block5e_se_expand (Conv2D)     (None, 1, 1, 816)    28560       ['block5e_se_reduce[0][0]']      
                                                                                                  
 block5e_se_excite (Multiply)   (None, 19, 19, 816)  0           ['block5e_activation[0][0]',     
                                                                  'block5e_se_expand[0][0]']      
                                                                                                  
 block5e_project_conv (Conv2D)  (None, 19, 19, 136)  110976      ['block5e_se_excite[0][0]']      
                                                                                                  
 block5e_project_bn (BatchNorma  (None, 19, 19, 136)  544        ['block5e_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5e_drop (FixedDropout)    (None, 19, 19, 136)  0           ['block5e_project_bn[0][0]']     
                                                                                                  
 block5e_add (Add)              (None, 19, 19, 136)  0           ['block5e_drop[0][0]',           
                                                                  'block5d_add[0][0]']            
                                                                                                  
 block6a_expand_conv (Conv2D)   (None, 19, 19, 816)  110976      ['block5e_add[0][0]']            
                                                                                                  
 block6a_expand_bn (BatchNormal  (None, 19, 19, 816)  3264       ['block6a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6a_expand_activation (Act  (None, 19, 19, 816)  0          ['block6a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6a_dwconv (DepthwiseConv2  (None, 10, 10, 816)  20400      ['block6a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6a_bn (BatchNormalization  (None, 10, 10, 816)  3264       ['block6a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6a_activation (Activation  (None, 10, 10, 816)  0          ['block6a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6a_se_squeeze (GlobalAver  (None, 816)         0           ['block6a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6a_se_reshape (Reshape)   (None, 1, 1, 816)    0           ['block6a_se_squeeze[0][0]']     
                                                                                                  
 block6a_se_reduce (Conv2D)     (None, 1, 1, 34)     27778       ['block6a_se_reshape[0][0]']     
                                                                                                  
 block6a_se_expand (Conv2D)     (None, 1, 1, 816)    28560       ['block6a_se_reduce[0][0]']      
                                                                                                  
 block6a_se_excite (Multiply)   (None, 10, 10, 816)  0           ['block6a_activation[0][0]',     
                                                                  'block6a_se_expand[0][0]']      
                                                                                                  
 block6a_project_conv (Conv2D)  (None, 10, 10, 232)  189312      ['block6a_se_excite[0][0]']      
                                                                                                  
 block6a_project_bn (BatchNorma  (None, 10, 10, 232)  928        ['block6a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6b_expand_conv (Conv2D)   (None, 10, 10, 1392  322944      ['block6a_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block6b_expand_bn (BatchNormal  (None, 10, 10, 1392  5568       ['block6b_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6b_expand_activation (Act  (None, 10, 10, 1392  0          ['block6b_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6b_dwconv (DepthwiseConv2  (None, 10, 10, 1392  34800      ['block6b_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6b_bn (BatchNormalization  (None, 10, 10, 1392  5568       ['block6b_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6b_activation (Activation  (None, 10, 10, 1392  0          ['block6b_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6b_se_squeeze (GlobalAver  (None, 1392)        0           ['block6b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6b_se_reshape (Reshape)   (None, 1, 1, 1392)   0           ['block6b_se_squeeze[0][0]']     
                                                                                                  
 block6b_se_reduce (Conv2D)     (None, 1, 1, 58)     80794       ['block6b_se_reshape[0][0]']     
                                                                                                  
 block6b_se_expand (Conv2D)     (None, 1, 1, 1392)   82128       ['block6b_se_reduce[0][0]']      
                                                                                                  
 block6b_se_excite (Multiply)   (None, 10, 10, 1392  0           ['block6b_activation[0][0]',     
                                )                                 'block6b_se_expand[0][0]']      
                                                                                                  
 block6b_project_conv (Conv2D)  (None, 10, 10, 232)  322944      ['block6b_se_excite[0][0]']      
                                                                                                  
 block6b_project_bn (BatchNorma  (None, 10, 10, 232)  928        ['block6b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6b_drop (FixedDropout)    (None, 10, 10, 232)  0           ['block6b_project_bn[0][0]']     
                                                                                                  
 block6b_add (Add)              (None, 10, 10, 232)  0           ['block6b_drop[0][0]',           
                                                                  'block6a_project_bn[0][0]']     
                                                                                                  
 block6c_expand_conv (Conv2D)   (None, 10, 10, 1392  322944      ['block6b_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6c_expand_bn (BatchNormal  (None, 10, 10, 1392  5568       ['block6c_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6c_expand_activation (Act  (None, 10, 10, 1392  0          ['block6c_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6c_dwconv (DepthwiseConv2  (None, 10, 10, 1392  34800      ['block6c_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6c_bn (BatchNormalization  (None, 10, 10, 1392  5568       ['block6c_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6c_activation (Activation  (None, 10, 10, 1392  0          ['block6c_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6c_se_squeeze (GlobalAver  (None, 1392)        0           ['block6c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6c_se_reshape (Reshape)   (None, 1, 1, 1392)   0           ['block6c_se_squeeze[0][0]']     
                                                                                                  
 block6c_se_reduce (Conv2D)     (None, 1, 1, 58)     80794       ['block6c_se_reshape[0][0]']     
                                                                                                  
 block6c_se_expand (Conv2D)     (None, 1, 1, 1392)   82128       ['block6c_se_reduce[0][0]']      
                                                                                                  
 block6c_se_excite (Multiply)   (None, 10, 10, 1392  0           ['block6c_activation[0][0]',     
                                )                                 'block6c_se_expand[0][0]']      
                                                                                                  
 block6c_project_conv (Conv2D)  (None, 10, 10, 232)  322944      ['block6c_se_excite[0][0]']      
                                                                                                  
 block6c_project_bn (BatchNorma  (None, 10, 10, 232)  928        ['block6c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6c_drop (FixedDropout)    (None, 10, 10, 232)  0           ['block6c_project_bn[0][0]']     
                                                                                                  
 block6c_add (Add)              (None, 10, 10, 232)  0           ['block6c_drop[0][0]',           
                                                                  'block6b_add[0][0]']            
                                                                                                  
 block6d_expand_conv (Conv2D)   (None, 10, 10, 1392  322944      ['block6c_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6d_expand_bn (BatchNormal  (None, 10, 10, 1392  5568       ['block6d_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6d_expand_activation (Act  (None, 10, 10, 1392  0          ['block6d_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6d_dwconv (DepthwiseConv2  (None, 10, 10, 1392  34800      ['block6d_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6d_bn (BatchNormalization  (None, 10, 10, 1392  5568       ['block6d_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6d_activation (Activation  (None, 10, 10, 1392  0          ['block6d_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6d_se_squeeze (GlobalAver  (None, 1392)        0           ['block6d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6d_se_reshape (Reshape)   (None, 1, 1, 1392)   0           ['block6d_se_squeeze[0][0]']     
                                                                                                  
 block6d_se_reduce (Conv2D)     (None, 1, 1, 58)     80794       ['block6d_se_reshape[0][0]']     
                                                                                                  
 block6d_se_expand (Conv2D)     (None, 1, 1, 1392)   82128       ['block6d_se_reduce[0][0]']      
                                                                                                  
 block6d_se_excite (Multiply)   (None, 10, 10, 1392  0           ['block6d_activation[0][0]',     
                                )                                 'block6d_se_expand[0][0]']      
                                                                                                  
 block6d_project_conv (Conv2D)  (None, 10, 10, 232)  322944      ['block6d_se_excite[0][0]']      
                                                                                                  
 block6d_project_bn (BatchNorma  (None, 10, 10, 232)  928        ['block6d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6d_drop (FixedDropout)    (None, 10, 10, 232)  0           ['block6d_project_bn[0][0]']     
                                                                                                  
 block6d_add (Add)              (None, 10, 10, 232)  0           ['block6d_drop[0][0]',           
                                                                  'block6c_add[0][0]']            
                                                                                                  
 block6e_expand_conv (Conv2D)   (None, 10, 10, 1392  322944      ['block6d_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6e_expand_bn (BatchNormal  (None, 10, 10, 1392  5568       ['block6e_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6e_expand_activation (Act  (None, 10, 10, 1392  0          ['block6e_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6e_dwconv (DepthwiseConv2  (None, 10, 10, 1392  34800      ['block6e_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6e_bn (BatchNormalization  (None, 10, 10, 1392  5568       ['block6e_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6e_activation (Activation  (None, 10, 10, 1392  0          ['block6e_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6e_se_squeeze (GlobalAver  (None, 1392)        0           ['block6e_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6e_se_reshape (Reshape)   (None, 1, 1, 1392)   0           ['block6e_se_squeeze[0][0]']     
                                                                                                  
 block6e_se_reduce (Conv2D)     (None, 1, 1, 58)     80794       ['block6e_se_reshape[0][0]']     
                                                                                                  
 block6e_se_expand (Conv2D)     (None, 1, 1, 1392)   82128       ['block6e_se_reduce[0][0]']      
                                                                                                  
 block6e_se_excite (Multiply)   (None, 10, 10, 1392  0           ['block6e_activation[0][0]',     
                                )                                 'block6e_se_expand[0][0]']      
                                                                                                  
 block6e_project_conv (Conv2D)  (None, 10, 10, 232)  322944      ['block6e_se_excite[0][0]']      
                                                                                                  
 block6e_project_bn (BatchNorma  (None, 10, 10, 232)  928        ['block6e_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6e_drop (FixedDropout)    (None, 10, 10, 232)  0           ['block6e_project_bn[0][0]']     
                                                                                                  
 block6e_add (Add)              (None, 10, 10, 232)  0           ['block6e_drop[0][0]',           
                                                                  'block6d_add[0][0]']            
                                                                                                  
 block6f_expand_conv (Conv2D)   (None, 10, 10, 1392  322944      ['block6e_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6f_expand_bn (BatchNormal  (None, 10, 10, 1392  5568       ['block6f_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6f_expand_activation (Act  (None, 10, 10, 1392  0          ['block6f_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6f_dwconv (DepthwiseConv2  (None, 10, 10, 1392  34800      ['block6f_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6f_bn (BatchNormalization  (None, 10, 10, 1392  5568       ['block6f_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6f_activation (Activation  (None, 10, 10, 1392  0          ['block6f_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6f_se_squeeze (GlobalAver  (None, 1392)        0           ['block6f_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6f_se_reshape (Reshape)   (None, 1, 1, 1392)   0           ['block6f_se_squeeze[0][0]']     
                                                                                                  
 block6f_se_reduce (Conv2D)     (None, 1, 1, 58)     80794       ['block6f_se_reshape[0][0]']     
                                                                                                  
 block6f_se_expand (Conv2D)     (None, 1, 1, 1392)   82128       ['block6f_se_reduce[0][0]']      
                                                                                                  
 block6f_se_excite (Multiply)   (None, 10, 10, 1392  0           ['block6f_activation[0][0]',     
                                )                                 'block6f_se_expand[0][0]']      
                                                                                                  
 block6f_project_conv (Conv2D)  (None, 10, 10, 232)  322944      ['block6f_se_excite[0][0]']      
                                                                                                  
 block6f_project_bn (BatchNorma  (None, 10, 10, 232)  928        ['block6f_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6f_drop (FixedDropout)    (None, 10, 10, 232)  0           ['block6f_project_bn[0][0]']     
                                                                                                  
 block6f_add (Add)              (None, 10, 10, 232)  0           ['block6f_drop[0][0]',           
                                                                  'block6e_add[0][0]']            
                                                                                                  
 block7a_expand_conv (Conv2D)   (None, 10, 10, 1392  322944      ['block6f_add[0][0]']            
                                )                                                                 
                                                                                                  
 block7a_expand_bn (BatchNormal  (None, 10, 10, 1392  5568       ['block7a_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block7a_expand_activation (Act  (None, 10, 10, 1392  0          ['block7a_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block7a_dwconv (DepthwiseConv2  (None, 10, 10, 1392  12528      ['block7a_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block7a_bn (BatchNormalization  (None, 10, 10, 1392  5568       ['block7a_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block7a_activation (Activation  (None, 10, 10, 1392  0          ['block7a_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block7a_se_squeeze (GlobalAver  (None, 1392)        0           ['block7a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block7a_se_reshape (Reshape)   (None, 1, 1, 1392)   0           ['block7a_se_squeeze[0][0]']     
                                                                                                  
 block7a_se_reduce (Conv2D)     (None, 1, 1, 58)     80794       ['block7a_se_reshape[0][0]']     
                                                                                                  
 block7a_se_expand (Conv2D)     (None, 1, 1, 1392)   82128       ['block7a_se_reduce[0][0]']      
                                                                                                  
 block7a_se_excite (Multiply)   (None, 10, 10, 1392  0           ['block7a_activation[0][0]',     
                                )                                 'block7a_se_expand[0][0]']      
                                                                                                  
 block7a_project_conv (Conv2D)  (None, 10, 10, 384)  534528      ['block7a_se_excite[0][0]']      
                                                                                                  
 block7a_project_bn (BatchNorma  (None, 10, 10, 384)  1536       ['block7a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block7b_expand_conv (Conv2D)   (None, 10, 10, 2304  884736      ['block7a_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block7b_expand_bn (BatchNormal  (None, 10, 10, 2304  9216       ['block7b_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block7b_expand_activation (Act  (None, 10, 10, 2304  0          ['block7b_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block7b_dwconv (DepthwiseConv2  (None, 10, 10, 2304  20736      ['block7b_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block7b_bn (BatchNormalization  (None, 10, 10, 2304  9216       ['block7b_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block7b_activation (Activation  (None, 10, 10, 2304  0          ['block7b_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block7b_se_squeeze (GlobalAver  (None, 2304)        0           ['block7b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block7b_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block7b_se_squeeze[0][0]']     
                                                                                                  
 block7b_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block7b_se_reshape[0][0]']     
                                                                                                  
 block7b_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block7b_se_reduce[0][0]']      
                                                                                                  
 block7b_se_excite (Multiply)   (None, 10, 10, 2304  0           ['block7b_activation[0][0]',     
                                )                                 'block7b_se_expand[0][0]']      
                                                                                                  
 block7b_project_conv (Conv2D)  (None, 10, 10, 384)  884736      ['block7b_se_excite[0][0]']      
                                                                                                  
 block7b_project_bn (BatchNorma  (None, 10, 10, 384)  1536       ['block7b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block7b_drop (FixedDropout)    (None, 10, 10, 384)  0           ['block7b_project_bn[0][0]']     
                                                                                                  
 block7b_add (Add)              (None, 10, 10, 384)  0           ['block7b_drop[0][0]',           
                                                                  'block7a_project_bn[0][0]']     
                                                                                                  
 top_conv (Conv2D)              (None, 10, 10, 1536  589824      ['block7b_add[0][0]']            
                                )                                                                 
                                                                                                  
 top_bn (BatchNormalization)    (None, 10, 10, 1536  6144        ['top_conv[0][0]']               
                                )                                                                 
                                                                                                  
 top_activation (Activation)    (None, 10, 10, 1536  0           ['top_bn[0][0]']                 
                                )                                                                 
                                                                                                  
 flatten_3 (Flatten)            (None, 153600)       0           ['top_activation[0][0]']         
                                                                                                  
 dense_6 (Dense)                (None, 1024)         157287424   ['flatten_3[0][0]']              
                                                                                                  
 dropout_3 (Dropout)            (None, 1024)         0           ['dense_6[0][0]']                
                                                                                                  
 dense_7 (Dense)                (None, 1)            1025        ['dropout_3[0][0]']              
                                                                                                  
==================================================================================================
Total params: 168,071,977
Trainable params: 157,288,449
Non-trainable params: 10,783,528
__________________________________________________________________________________________________

#get total parameters
model_params = model_final.count_params()

# Specify the optimizer, loss function and evaluation metrics. 
model_final.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001), metrics=['accuracy'])

t1 = time.time()
#train the model
eff_history = model_final.fit_generator(train_generator, validation_data = validation_generator, steps_per_epoch = 100, epochs = 10)
fit_time = time.time() - t1

<ipython-input-85-b7f31b017b18>:3: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  eff_history = model_final.fit_generator(train_generator, validation_data = validation_generator, steps_per_epoch = 100, epochs = 10)

Epoch 1/10
100/100 [==============================] - 61s 504ms/step - loss: 0.5111 - accuracy: 0.9280 - val_loss: 0.0415 - val_accuracy: 0.9937
Epoch 2/10
100/100 [==============================] - 47s 466ms/step - loss: 0.4081 - accuracy: 0.9620 - val_loss: 0.0409 - val_accuracy: 0.9900
Epoch 3/10
100/100 [==============================] - 46s 461ms/step - loss: 0.4214 - accuracy: 0.9585 - val_loss: 0.0315 - val_accuracy: 0.9912
Epoch 4/10
100/100 [==============================] - 47s 464ms/step - loss: 0.3733 - accuracy: 0.9632 - val_loss: 0.0365 - val_accuracy: 0.9937
Epoch 5/10
100/100 [==============================] - 47s 465ms/step - loss: 0.3327 - accuracy: 0.9620 - val_loss: 0.0403 - val_accuracy: 0.9925
Epoch 6/10
100/100 [==============================] - 47s 469ms/step - loss: 0.3235 - accuracy: 0.9657 - val_loss: 0.0388 - val_accuracy: 0.9925
Epoch 7/10
100/100 [==============================] - 47s 468ms/step - loss: 0.4184 - accuracy: 0.9620 - val_loss: 0.0627 - val_accuracy: 0.9887
Epoch 8/10
100/100 [==============================] - 47s 465ms/step - loss: 0.3922 - accuracy: 0.9655 - val_loss: 0.0273 - val_accuracy: 0.9937
Epoch 9/10
100/100 [==============================] - 47s 464ms/step - loss: 0.3318 - accuracy: 0.9703 - val_loss: 0.0106 - val_accuracy: 0.9962
Epoch 10/10
100/100 [==============================] - 46s 460ms/step - loss: 0.6514 - accuracy: 0.9627 - val_loss: 0.0082 - val_accuracy: 0.9975

# time it took to fit the model
print(fit_time) 

516.3287916183472

#Plot training and validation accuracy and loss for each epoch
acc = eff_history.history['accuracy']
val_acc = eff_history.history['val_accuracy']
loss = eff_history.history['loss']
val_loss = eff_history.history['val_loss']

epochs = range(1,len(acc) + 1)

plt.plot(epochs,acc,label = 'Training Accuracy')
plt.plot(epochs,val_acc,label = 'Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()
plt.figure()

plt.plot(epochs,loss,label = 'Training loss')
plt.plot(epochs,val_loss,label = 'Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()

plt.show()

# Test dataset 
test_datagen = ImageDataGenerator(rescale=1./255)

test_generator = test_datagen.flow_from_directory(
        test_dir,
        target_size=(300, 300),
        shuffle = False,
        class_mode='binary',
        batch_size=1)

Found 2023 images belonging to 2 classes.

#Get test length
filenames = test_generator.filenames
nb_samples = len(filenames)

#Predict on test set
predict = model_final.predict_generator(test_generator,steps = nb_samples)

<ipython-input-90-7710eff794cf>:2: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.
  predict = model_final.predict_generator(test_generator,steps = nb_samples)

#Get list of prediction results
pred_list = []
for i in predict:
    if i > 0.5:
        result = 1 #dog
        pred_list.append(result)
    else:
      result = 0 #cat
      pred_list.append(result)

#Create dataframe of image ID, image true label, image predicted label
import pandas as pd
image_ids = [name.split('/')[-1] for name in test_generator.filenames]
image_label = [name.split('/')[0] for name in test_generator.filenames]
data = {'id': image_ids, 'label':image_label, 'prediction':pred_list} 
data_df = pd.DataFrame(data)
data_df.label.replace(('cats', 'dogs'), (0, 1), inplace=True) # change cat and dog label to 0 or 1

#Get test accuracy score
from sklearn.metrics import accuracy_score, confusion_matrix
test_accuracy = accuracy_score(data_df['label'], data_df['prediction'])
print('Test Accuracy: ', round((test_accuracy * 100), 2), "%")

Test Accuracy:  98.86 %

from sklearn.metrics import classification_report
#Classification Report
print(classification_report(data_df['label'], data_df['prediction']))

              precision    recall  f1-score   support

           0       0.99      0.98      0.99      1011
           1       0.98      0.99      0.99      1012

    accuracy                           0.99      2023
   macro avg       0.99      0.99      0.99      2023
weighted avg       0.99      0.99      0.99      2023

#Create confusion matrix
import seaborn as sns
label = [0, 1] #0 = cat and 1 = dog
cm = confusion_matrix(data_df['label'], data_df['prediction'], labels = label)
#Plot
ax= plt.subplot()
sns.heatmap(cm, annot=True, fmt='g', ax=ax);
# labels, title and ticks
ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); 
ax.set_title('Confusion Matrix'); 
ax.xaxis.set_ticklabels(["Cat", "Dog"]); ax.yaxis.set_ticklabels(["Cat", "Dog"])

[Text(0, 0.5, 'Cat'), Text(0, 1.5, 'Dog')]

ExperimentLog.loc[len(ExperimentLog)] = [
    "EfficientNet B3",
    300,
    "RMSprop", 
    10, 
    max(acc), 
    max(val_acc),
    test_accuracy,
    fit_time,
    model_params
]

ExperimentLog 

	Base Model	Input Resolution	Optimizer	Epochs	Training Accuracy	Validation Accuracy	Test Accuracy	Fit Time	Total Parameters
0	EfficientNet B0	224	RMSprop	10	0.951500	0.98750	0.971330	2195.619411	68276893
1	EfficientNet B0 with decay	224	RMSprop	10	0.956171	0.98750	0.974790	381.655278	68276893
2	EfficientNet B1	240	RMSprop	10	0.959698	0.99125	0.985171	349.486195	90463361
3	EfficientNet B2	260	RMSprop	10	0.965239	0.99500	0.983193	380.205944	124555763
4	EfficientNet B3	300	RMSprop	10	0.970277	0.99750	0.988631	516.328792	168071977

EfficientNetB4

# Add rescaling and augmentation to ImageDataGenerator for the training set
train_datagen = ImageDataGenerator(rescale = 1./255., rotation_range = 40, width_shift_range = 0.2, height_shift_range = 0.2, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True, validation_split=0.1) # set validation split
# Rescale validation set. No augmentation on the validation set. 
validation_datagen = ImageDataGenerator(rescale = 1./255.,validation_split=0.1) # set validation split
#Read images directly from directory. 
train_generator = train_datagen.flow_from_directory(train_dir, seed = 42, shuffle = True, batch_size = 20, class_mode = 'binary', target_size = (380, 380), subset='training') #set as training data
validation_generator = validation_datagen.flow_from_directory(train_dir, seed = 42, shuffle = True, batch_size = 20, class_mode = 'binary', target_size = (380, 380), subset='validation') # same directory as training data. Set as validation data

Found 7205 images belonging to 2 classes.
Found 800 images belonging to 2 classes.

#Instantiates the EfficientNet architecture
base_model = efn.EfficientNetB4(input_shape = (380, 380, 3), include_top = False, weights = 'imagenet')

Downloading data from https://github.com/Callidior/keras-applications/releases/download/efficientnet/efficientnet-b4_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5
71892840/71892840 [==============================] - 4s 0us/step

# Set trainable attribute to false for all of the base model layers
for layer in base_model.layers:
    layer.trainable = False

#Build on top of existing base model. 
x = base_model.output
x = layers.Flatten()(x) #convert to 1D array
x = layers.Dense(1024, activation="relu")(x) #fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dropout(0.5)(x) #Drops 50% of inputs to zero at each training iteration (prevents overfitting)

# Add a final sigmoid layer with 1 node for classification output (probability between 0 and 1)
predictions = layers.Dense(1, activation="sigmoid")(x)
model_final = Model(inputs = base_model.input, outputs = predictions)

#Print model summary
from torchsummary import summary
model_sum = model_final.summary()

Model: "model_4"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_5 (InputLayer)           [(None, 380, 380, 3  0           []                               
                                )]                                                                
                                                                                                  
 stem_conv (Conv2D)             (None, 190, 190, 48  1296        ['input_5[0][0]']                
                                )                                                                 
                                                                                                  
 stem_bn (BatchNormalization)   (None, 190, 190, 48  192         ['stem_conv[0][0]']              
                                )                                                                 
                                                                                                  
 stem_activation (Activation)   (None, 190, 190, 48  0           ['stem_bn[0][0]']                
                                )                                                                 
                                                                                                  
 block1a_dwconv (DepthwiseConv2  (None, 190, 190, 48  432        ['stem_activation[0][0]']        
 D)                             )                                                                 
                                                                                                  
 block1a_bn (BatchNormalization  (None, 190, 190, 48  192        ['block1a_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block1a_activation (Activation  (None, 190, 190, 48  0          ['block1a_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block1a_se_squeeze (GlobalAver  (None, 48)          0           ['block1a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block1a_se_reshape (Reshape)   (None, 1, 1, 48)     0           ['block1a_se_squeeze[0][0]']     
                                                                                                  
 block1a_se_reduce (Conv2D)     (None, 1, 1, 12)     588         ['block1a_se_reshape[0][0]']     
                                                                                                  
 block1a_se_expand (Conv2D)     (None, 1, 1, 48)     624         ['block1a_se_reduce[0][0]']      
                                                                                                  
 block1a_se_excite (Multiply)   (None, 190, 190, 48  0           ['block1a_activation[0][0]',     
                                )                                 'block1a_se_expand[0][0]']      
                                                                                                  
 block1a_project_conv (Conv2D)  (None, 190, 190, 24  1152        ['block1a_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block1a_project_bn (BatchNorma  (None, 190, 190, 24  96         ['block1a_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block1b_dwconv (DepthwiseConv2  (None, 190, 190, 24  216        ['block1a_project_bn[0][0]']     
 D)                             )                                                                 
                                                                                                  
 block1b_bn (BatchNormalization  (None, 190, 190, 24  96         ['block1b_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block1b_activation (Activation  (None, 190, 190, 24  0          ['block1b_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block1b_se_squeeze (GlobalAver  (None, 24)          0           ['block1b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block1b_se_reshape (Reshape)   (None, 1, 1, 24)     0           ['block1b_se_squeeze[0][0]']     
                                                                                                  
 block1b_se_reduce (Conv2D)     (None, 1, 1, 6)      150         ['block1b_se_reshape[0][0]']     
                                                                                                  
 block1b_se_expand (Conv2D)     (None, 1, 1, 24)     168         ['block1b_se_reduce[0][0]']      
                                                                                                  
 block1b_se_excite (Multiply)   (None, 190, 190, 24  0           ['block1b_activation[0][0]',     
                                )                                 'block1b_se_expand[0][0]']      
                                                                                                  
 block1b_project_conv (Conv2D)  (None, 190, 190, 24  576         ['block1b_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block1b_project_bn (BatchNorma  (None, 190, 190, 24  96         ['block1b_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block1b_drop (FixedDropout)    (None, 190, 190, 24  0           ['block1b_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block1b_add (Add)              (None, 190, 190, 24  0           ['block1b_drop[0][0]',           
                                )                                 'block1a_project_bn[0][0]']     
                                                                                                  
 block2a_expand_conv (Conv2D)   (None, 190, 190, 14  3456        ['block1b_add[0][0]']            
                                4)                                                                
                                                                                                  
 block2a_expand_bn (BatchNormal  (None, 190, 190, 14  576        ['block2a_expand_conv[0][0]']    
 ization)                       4)                                                                
                                                                                                  
 block2a_expand_activation (Act  (None, 190, 190, 14  0          ['block2a_expand_bn[0][0]']      
 ivation)                       4)                                                                
                                                                                                  
 block2a_dwconv (DepthwiseConv2  (None, 95, 95, 144)  1296       ['block2a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2a_bn (BatchNormalization  (None, 95, 95, 144)  576        ['block2a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2a_activation (Activation  (None, 95, 95, 144)  0          ['block2a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2a_se_squeeze (GlobalAver  (None, 144)         0           ['block2a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2a_se_squeeze[0][0]']     
                                                                                                  
 block2a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2a_se_reshape[0][0]']     
                                                                                                  
 block2a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2a_se_reduce[0][0]']      
                                                                                                  
 block2a_se_excite (Multiply)   (None, 95, 95, 144)  0           ['block2a_activation[0][0]',     
                                                                  'block2a_se_expand[0][0]']      
                                                                                                  
 block2a_project_conv (Conv2D)  (None, 95, 95, 32)   4608        ['block2a_se_excite[0][0]']      
                                                                                                  
 block2a_project_bn (BatchNorma  (None, 95, 95, 32)  128         ['block2a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2b_expand_conv (Conv2D)   (None, 95, 95, 192)  6144        ['block2a_project_bn[0][0]']     
                                                                                                  
 block2b_expand_bn (BatchNormal  (None, 95, 95, 192)  768        ['block2b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block2b_expand_activation (Act  (None, 95, 95, 192)  0          ['block2b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block2b_dwconv (DepthwiseConv2  (None, 95, 95, 192)  1728       ['block2b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2b_bn (BatchNormalization  (None, 95, 95, 192)  768        ['block2b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2b_activation (Activation  (None, 95, 95, 192)  0          ['block2b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2b_se_squeeze (GlobalAver  (None, 192)         0           ['block2b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2b_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2b_se_squeeze[0][0]']     
                                                                                                  
 block2b_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2b_se_reshape[0][0]']     
                                                                                                  
 block2b_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2b_se_reduce[0][0]']      
                                                                                                  
 block2b_se_excite (Multiply)   (None, 95, 95, 192)  0           ['block2b_activation[0][0]',     
                                                                  'block2b_se_expand[0][0]']      
                                                                                                  
 block2b_project_conv (Conv2D)  (None, 95, 95, 32)   6144        ['block2b_se_excite[0][0]']      
                                                                                                  
 block2b_project_bn (BatchNorma  (None, 95, 95, 32)  128         ['block2b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2b_drop (FixedDropout)    (None, 95, 95, 32)   0           ['block2b_project_bn[0][0]']     
                                                                                                  
 block2b_add (Add)              (None, 95, 95, 32)   0           ['block2b_drop[0][0]',           
                                                                  'block2a_project_bn[0][0]']     
                                                                                                  
 block2c_expand_conv (Conv2D)   (None, 95, 95, 192)  6144        ['block2b_add[0][0]']            
                                                                                                  
 block2c_expand_bn (BatchNormal  (None, 95, 95, 192)  768        ['block2c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block2c_expand_activation (Act  (None, 95, 95, 192)  0          ['block2c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block2c_dwconv (DepthwiseConv2  (None, 95, 95, 192)  1728       ['block2c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2c_bn (BatchNormalization  (None, 95, 95, 192)  768        ['block2c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2c_activation (Activation  (None, 95, 95, 192)  0          ['block2c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2c_se_squeeze (GlobalAver  (None, 192)         0           ['block2c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2c_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2c_se_squeeze[0][0]']     
                                                                                                  
 block2c_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2c_se_reshape[0][0]']     
                                                                                                  
 block2c_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2c_se_reduce[0][0]']      
                                                                                                  
 block2c_se_excite (Multiply)   (None, 95, 95, 192)  0           ['block2c_activation[0][0]',     
                                                                  'block2c_se_expand[0][0]']      
                                                                                                  
 block2c_project_conv (Conv2D)  (None, 95, 95, 32)   6144        ['block2c_se_excite[0][0]']      
                                                                                                  
 block2c_project_bn (BatchNorma  (None, 95, 95, 32)  128         ['block2c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2c_drop (FixedDropout)    (None, 95, 95, 32)   0           ['block2c_project_bn[0][0]']     
                                                                                                  
 block2c_add (Add)              (None, 95, 95, 32)   0           ['block2c_drop[0][0]',           
                                                                  'block2b_add[0][0]']            
                                                                                                  
 block2d_expand_conv (Conv2D)   (None, 95, 95, 192)  6144        ['block2c_add[0][0]']            
                                                                                                  
 block2d_expand_bn (BatchNormal  (None, 95, 95, 192)  768        ['block2d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block2d_expand_activation (Act  (None, 95, 95, 192)  0          ['block2d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block2d_dwconv (DepthwiseConv2  (None, 95, 95, 192)  1728       ['block2d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2d_bn (BatchNormalization  (None, 95, 95, 192)  768        ['block2d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2d_activation (Activation  (None, 95, 95, 192)  0          ['block2d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2d_se_squeeze (GlobalAver  (None, 192)         0           ['block2d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2d_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2d_se_squeeze[0][0]']     
                                                                                                  
 block2d_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2d_se_reshape[0][0]']     
                                                                                                  
 block2d_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2d_se_reduce[0][0]']      
                                                                                                  
 block2d_se_excite (Multiply)   (None, 95, 95, 192)  0           ['block2d_activation[0][0]',     
                                                                  'block2d_se_expand[0][0]']      
                                                                                                  
 block2d_project_conv (Conv2D)  (None, 95, 95, 32)   6144        ['block2d_se_excite[0][0]']      
                                                                                                  
 block2d_project_bn (BatchNorma  (None, 95, 95, 32)  128         ['block2d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2d_drop (FixedDropout)    (None, 95, 95, 32)   0           ['block2d_project_bn[0][0]']     
                                                                                                  
 block2d_add (Add)              (None, 95, 95, 32)   0           ['block2d_drop[0][0]',           
                                                                  'block2c_add[0][0]']            
                                                                                                  
 block3a_expand_conv (Conv2D)   (None, 95, 95, 192)  6144        ['block2d_add[0][0]']            
                                                                                                  
 block3a_expand_bn (BatchNormal  (None, 95, 95, 192)  768        ['block3a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3a_expand_activation (Act  (None, 95, 95, 192)  0          ['block3a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3a_dwconv (DepthwiseConv2  (None, 48, 48, 192)  4800       ['block3a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3a_bn (BatchNormalization  (None, 48, 48, 192)  768        ['block3a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3a_activation (Activation  (None, 48, 48, 192)  0          ['block3a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3a_se_squeeze (GlobalAver  (None, 192)         0           ['block3a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3a_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block3a_se_squeeze[0][0]']     
                                                                                                  
 block3a_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block3a_se_reshape[0][0]']     
                                                                                                  
 block3a_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block3a_se_reduce[0][0]']      
                                                                                                  
 block3a_se_excite (Multiply)   (None, 48, 48, 192)  0           ['block3a_activation[0][0]',     
                                                                  'block3a_se_expand[0][0]']      
                                                                                                  
 block3a_project_conv (Conv2D)  (None, 48, 48, 56)   10752       ['block3a_se_excite[0][0]']      
                                                                                                  
 block3a_project_bn (BatchNorma  (None, 48, 48, 56)  224         ['block3a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3b_expand_conv (Conv2D)   (None, 48, 48, 336)  18816       ['block3a_project_bn[0][0]']     
                                                                                                  
 block3b_expand_bn (BatchNormal  (None, 48, 48, 336)  1344       ['block3b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3b_expand_activation (Act  (None, 48, 48, 336)  0          ['block3b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3b_dwconv (DepthwiseConv2  (None, 48, 48, 336)  8400       ['block3b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3b_bn (BatchNormalization  (None, 48, 48, 336)  1344       ['block3b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3b_activation (Activation  (None, 48, 48, 336)  0          ['block3b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3b_se_squeeze (GlobalAver  (None, 336)         0           ['block3b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3b_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block3b_se_squeeze[0][0]']     
                                                                                                  
 block3b_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block3b_se_reshape[0][0]']     
                                                                                                  
 block3b_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block3b_se_reduce[0][0]']      
                                                                                                  
 block3b_se_excite (Multiply)   (None, 48, 48, 336)  0           ['block3b_activation[0][0]',     
                                                                  'block3b_se_expand[0][0]']      
                                                                                                  
 block3b_project_conv (Conv2D)  (None, 48, 48, 56)   18816       ['block3b_se_excite[0][0]']      
                                                                                                  
 block3b_project_bn (BatchNorma  (None, 48, 48, 56)  224         ['block3b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3b_drop (FixedDropout)    (None, 48, 48, 56)   0           ['block3b_project_bn[0][0]']     
                                                                                                  
 block3b_add (Add)              (None, 48, 48, 56)   0           ['block3b_drop[0][0]',           
                                                                  'block3a_project_bn[0][0]']     
                                                                                                  
 block3c_expand_conv (Conv2D)   (None, 48, 48, 336)  18816       ['block3b_add[0][0]']            
                                                                                                  
 block3c_expand_bn (BatchNormal  (None, 48, 48, 336)  1344       ['block3c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3c_expand_activation (Act  (None, 48, 48, 336)  0          ['block3c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3c_dwconv (DepthwiseConv2  (None, 48, 48, 336)  8400       ['block3c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3c_bn (BatchNormalization  (None, 48, 48, 336)  1344       ['block3c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3c_activation (Activation  (None, 48, 48, 336)  0          ['block3c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3c_se_squeeze (GlobalAver  (None, 336)         0           ['block3c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3c_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block3c_se_squeeze[0][0]']     
                                                                                                  
 block3c_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block3c_se_reshape[0][0]']     
                                                                                                  
 block3c_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block3c_se_reduce[0][0]']      
                                                                                                  
 block3c_se_excite (Multiply)   (None, 48, 48, 336)  0           ['block3c_activation[0][0]',     
                                                                  'block3c_se_expand[0][0]']      
                                                                                                  
 block3c_project_conv (Conv2D)  (None, 48, 48, 56)   18816       ['block3c_se_excite[0][0]']      
                                                                                                  
 block3c_project_bn (BatchNorma  (None, 48, 48, 56)  224         ['block3c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3c_drop (FixedDropout)    (None, 48, 48, 56)   0           ['block3c_project_bn[0][0]']     
                                                                                                  
 block3c_add (Add)              (None, 48, 48, 56)   0           ['block3c_drop[0][0]',           
                                                                  'block3b_add[0][0]']            
                                                                                                  
 block3d_expand_conv (Conv2D)   (None, 48, 48, 336)  18816       ['block3c_add[0][0]']            
                                                                                                  
 block3d_expand_bn (BatchNormal  (None, 48, 48, 336)  1344       ['block3d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3d_expand_activation (Act  (None, 48, 48, 336)  0          ['block3d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3d_dwconv (DepthwiseConv2  (None, 48, 48, 336)  8400       ['block3d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3d_bn (BatchNormalization  (None, 48, 48, 336)  1344       ['block3d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3d_activation (Activation  (None, 48, 48, 336)  0          ['block3d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3d_se_squeeze (GlobalAver  (None, 336)         0           ['block3d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3d_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block3d_se_squeeze[0][0]']     
                                                                                                  
 block3d_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block3d_se_reshape[0][0]']     
                                                                                                  
 block3d_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block3d_se_reduce[0][0]']      
                                                                                                  
 block3d_se_excite (Multiply)   (None, 48, 48, 336)  0           ['block3d_activation[0][0]',     
                                                                  'block3d_se_expand[0][0]']      
                                                                                                  
 block3d_project_conv (Conv2D)  (None, 48, 48, 56)   18816       ['block3d_se_excite[0][0]']      
                                                                                                  
 block3d_project_bn (BatchNorma  (None, 48, 48, 56)  224         ['block3d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3d_drop (FixedDropout)    (None, 48, 48, 56)   0           ['block3d_project_bn[0][0]']     
                                                                                                  
 block3d_add (Add)              (None, 48, 48, 56)   0           ['block3d_drop[0][0]',           
                                                                  'block3c_add[0][0]']            
                                                                                                  
 block4a_expand_conv (Conv2D)   (None, 48, 48, 336)  18816       ['block3d_add[0][0]']            
                                                                                                  
 block4a_expand_bn (BatchNormal  (None, 48, 48, 336)  1344       ['block4a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4a_expand_activation (Act  (None, 48, 48, 336)  0          ['block4a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4a_dwconv (DepthwiseConv2  (None, 24, 24, 336)  3024       ['block4a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4a_bn (BatchNormalization  (None, 24, 24, 336)  1344       ['block4a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4a_activation (Activation  (None, 24, 24, 336)  0          ['block4a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4a_se_squeeze (GlobalAver  (None, 336)         0           ['block4a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4a_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block4a_se_squeeze[0][0]']     
                                                                                                  
 block4a_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block4a_se_reshape[0][0]']     
                                                                                                  
 block4a_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block4a_se_reduce[0][0]']      
                                                                                                  
 block4a_se_excite (Multiply)   (None, 24, 24, 336)  0           ['block4a_activation[0][0]',     
                                                                  'block4a_se_expand[0][0]']      
                                                                                                  
 block4a_project_conv (Conv2D)  (None, 24, 24, 112)  37632       ['block4a_se_excite[0][0]']      
                                                                                                  
 block4a_project_bn (BatchNorma  (None, 24, 24, 112)  448        ['block4a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4b_expand_conv (Conv2D)   (None, 24, 24, 672)  75264       ['block4a_project_bn[0][0]']     
                                                                                                  
 block4b_expand_bn (BatchNormal  (None, 24, 24, 672)  2688       ['block4b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4b_expand_activation (Act  (None, 24, 24, 672)  0          ['block4b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4b_dwconv (DepthwiseConv2  (None, 24, 24, 672)  6048       ['block4b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4b_bn (BatchNormalization  (None, 24, 24, 672)  2688       ['block4b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4b_activation (Activation  (None, 24, 24, 672)  0          ['block4b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4b_se_squeeze (GlobalAver  (None, 672)         0           ['block4b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4b_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4b_se_squeeze[0][0]']     
                                                                                                  
 block4b_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4b_se_reshape[0][0]']     
                                                                                                  
 block4b_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4b_se_reduce[0][0]']      
                                                                                                  
 block4b_se_excite (Multiply)   (None, 24, 24, 672)  0           ['block4b_activation[0][0]',     
                                                                  'block4b_se_expand[0][0]']      
                                                                                                  
 block4b_project_conv (Conv2D)  (None, 24, 24, 112)  75264       ['block4b_se_excite[0][0]']      
                                                                                                  
 block4b_project_bn (BatchNorma  (None, 24, 24, 112)  448        ['block4b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4b_drop (FixedDropout)    (None, 24, 24, 112)  0           ['block4b_project_bn[0][0]']     
                                                                                                  
 block4b_add (Add)              (None, 24, 24, 112)  0           ['block4b_drop[0][0]',           
                                                                  'block4a_project_bn[0][0]']     
                                                                                                  
 block4c_expand_conv (Conv2D)   (None, 24, 24, 672)  75264       ['block4b_add[0][0]']            
                                                                                                  
 block4c_expand_bn (BatchNormal  (None, 24, 24, 672)  2688       ['block4c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4c_expand_activation (Act  (None, 24, 24, 672)  0          ['block4c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4c_dwconv (DepthwiseConv2  (None, 24, 24, 672)  6048       ['block4c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4c_bn (BatchNormalization  (None, 24, 24, 672)  2688       ['block4c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4c_activation (Activation  (None, 24, 24, 672)  0          ['block4c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4c_se_squeeze (GlobalAver  (None, 672)         0           ['block4c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4c_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4c_se_squeeze[0][0]']     
                                                                                                  
 block4c_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4c_se_reshape[0][0]']     
                                                                                                  
 block4c_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4c_se_reduce[0][0]']      
                                                                                                  
 block4c_se_excite (Multiply)   (None, 24, 24, 672)  0           ['block4c_activation[0][0]',     
                                                                  'block4c_se_expand[0][0]']      
                                                                                                  
 block4c_project_conv (Conv2D)  (None, 24, 24, 112)  75264       ['block4c_se_excite[0][0]']      
                                                                                                  
 block4c_project_bn (BatchNorma  (None, 24, 24, 112)  448        ['block4c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4c_drop (FixedDropout)    (None, 24, 24, 112)  0           ['block4c_project_bn[0][0]']     
                                                                                                  
 block4c_add (Add)              (None, 24, 24, 112)  0           ['block4c_drop[0][0]',           
                                                                  'block4b_add[0][0]']            
                                                                                                  
 block4d_expand_conv (Conv2D)   (None, 24, 24, 672)  75264       ['block4c_add[0][0]']            
                                                                                                  
 block4d_expand_bn (BatchNormal  (None, 24, 24, 672)  2688       ['block4d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4d_expand_activation (Act  (None, 24, 24, 672)  0          ['block4d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4d_dwconv (DepthwiseConv2  (None, 24, 24, 672)  6048       ['block4d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4d_bn (BatchNormalization  (None, 24, 24, 672)  2688       ['block4d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4d_activation (Activation  (None, 24, 24, 672)  0          ['block4d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4d_se_squeeze (GlobalAver  (None, 672)         0           ['block4d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4d_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4d_se_squeeze[0][0]']     
                                                                                                  
 block4d_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4d_se_reshape[0][0]']     
                                                                                                  
 block4d_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4d_se_reduce[0][0]']      
                                                                                                  
 block4d_se_excite (Multiply)   (None, 24, 24, 672)  0           ['block4d_activation[0][0]',     
                                                                  'block4d_se_expand[0][0]']      
                                                                                                  
 block4d_project_conv (Conv2D)  (None, 24, 24, 112)  75264       ['block4d_se_excite[0][0]']      
                                                                                                  
 block4d_project_bn (BatchNorma  (None, 24, 24, 112)  448        ['block4d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4d_drop (FixedDropout)    (None, 24, 24, 112)  0           ['block4d_project_bn[0][0]']     
                                                                                                  
 block4d_add (Add)              (None, 24, 24, 112)  0           ['block4d_drop[0][0]',           
                                                                  'block4c_add[0][0]']            
                                                                                                  
 block4e_expand_conv (Conv2D)   (None, 24, 24, 672)  75264       ['block4d_add[0][0]']            
                                                                                                  
 block4e_expand_bn (BatchNormal  (None, 24, 24, 672)  2688       ['block4e_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4e_expand_activation (Act  (None, 24, 24, 672)  0          ['block4e_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4e_dwconv (DepthwiseConv2  (None, 24, 24, 672)  6048       ['block4e_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4e_bn (BatchNormalization  (None, 24, 24, 672)  2688       ['block4e_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4e_activation (Activation  (None, 24, 24, 672)  0          ['block4e_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4e_se_squeeze (GlobalAver  (None, 672)         0           ['block4e_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4e_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4e_se_squeeze[0][0]']     
                                                                                                  
 block4e_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4e_se_reshape[0][0]']     
                                                                                                  
 block4e_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4e_se_reduce[0][0]']      
                                                                                                  
 block4e_se_excite (Multiply)   (None, 24, 24, 672)  0           ['block4e_activation[0][0]',     
                                                                  'block4e_se_expand[0][0]']      
                                                                                                  
 block4e_project_conv (Conv2D)  (None, 24, 24, 112)  75264       ['block4e_se_excite[0][0]']      
                                                                                                  
 block4e_project_bn (BatchNorma  (None, 24, 24, 112)  448        ['block4e_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4e_drop (FixedDropout)    (None, 24, 24, 112)  0           ['block4e_project_bn[0][0]']     
                                                                                                  
 block4e_add (Add)              (None, 24, 24, 112)  0           ['block4e_drop[0][0]',           
                                                                  'block4d_add[0][0]']            
                                                                                                  
 block4f_expand_conv (Conv2D)   (None, 24, 24, 672)  75264       ['block4e_add[0][0]']            
                                                                                                  
 block4f_expand_bn (BatchNormal  (None, 24, 24, 672)  2688       ['block4f_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4f_expand_activation (Act  (None, 24, 24, 672)  0          ['block4f_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4f_dwconv (DepthwiseConv2  (None, 24, 24, 672)  6048       ['block4f_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4f_bn (BatchNormalization  (None, 24, 24, 672)  2688       ['block4f_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4f_activation (Activation  (None, 24, 24, 672)  0          ['block4f_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4f_se_squeeze (GlobalAver  (None, 672)         0           ['block4f_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4f_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4f_se_squeeze[0][0]']     
                                                                                                  
 block4f_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4f_se_reshape[0][0]']     
                                                                                                  
 block4f_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4f_se_reduce[0][0]']      
                                                                                                  
 block4f_se_excite (Multiply)   (None, 24, 24, 672)  0           ['block4f_activation[0][0]',     
                                                                  'block4f_se_expand[0][0]']      
                                                                                                  
 block4f_project_conv (Conv2D)  (None, 24, 24, 112)  75264       ['block4f_se_excite[0][0]']      
                                                                                                  
 block4f_project_bn (BatchNorma  (None, 24, 24, 112)  448        ['block4f_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4f_drop (FixedDropout)    (None, 24, 24, 112)  0           ['block4f_project_bn[0][0]']     
                                                                                                  
 block4f_add (Add)              (None, 24, 24, 112)  0           ['block4f_drop[0][0]',           
                                                                  'block4e_add[0][0]']            
                                                                                                  
 block5a_expand_conv (Conv2D)   (None, 24, 24, 672)  75264       ['block4f_add[0][0]']            
                                                                                                  
 block5a_expand_bn (BatchNormal  (None, 24, 24, 672)  2688       ['block5a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5a_expand_activation (Act  (None, 24, 24, 672)  0          ['block5a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5a_dwconv (DepthwiseConv2  (None, 24, 24, 672)  16800      ['block5a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5a_bn (BatchNormalization  (None, 24, 24, 672)  2688       ['block5a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5a_activation (Activation  (None, 24, 24, 672)  0          ['block5a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5a_se_squeeze (GlobalAver  (None, 672)         0           ['block5a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5a_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5a_se_squeeze[0][0]']     
                                                                                                  
 block5a_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5a_se_reshape[0][0]']     
                                                                                                  
 block5a_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5a_se_reduce[0][0]']      
                                                                                                  
 block5a_se_excite (Multiply)   (None, 24, 24, 672)  0           ['block5a_activation[0][0]',     
                                                                  'block5a_se_expand[0][0]']      
                                                                                                  
 block5a_project_conv (Conv2D)  (None, 24, 24, 160)  107520      ['block5a_se_excite[0][0]']      
                                                                                                  
 block5a_project_bn (BatchNorma  (None, 24, 24, 160)  640        ['block5a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5b_expand_conv (Conv2D)   (None, 24, 24, 960)  153600      ['block5a_project_bn[0][0]']     
                                                                                                  
 block5b_expand_bn (BatchNormal  (None, 24, 24, 960)  3840       ['block5b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5b_expand_activation (Act  (None, 24, 24, 960)  0          ['block5b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5b_dwconv (DepthwiseConv2  (None, 24, 24, 960)  24000      ['block5b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5b_bn (BatchNormalization  (None, 24, 24, 960)  3840       ['block5b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5b_activation (Activation  (None, 24, 24, 960)  0          ['block5b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5b_se_squeeze (GlobalAver  (None, 960)         0           ['block5b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5b_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5b_se_squeeze[0][0]']     
                                                                                                  
 block5b_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5b_se_reshape[0][0]']     
                                                                                                  
 block5b_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5b_se_reduce[0][0]']      
                                                                                                  
 block5b_se_excite (Multiply)   (None, 24, 24, 960)  0           ['block5b_activation[0][0]',     
                                                                  'block5b_se_expand[0][0]']      
                                                                                                  
 block5b_project_conv (Conv2D)  (None, 24, 24, 160)  153600      ['block5b_se_excite[0][0]']      
                                                                                                  
 block5b_project_bn (BatchNorma  (None, 24, 24, 160)  640        ['block5b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5b_drop (FixedDropout)    (None, 24, 24, 160)  0           ['block5b_project_bn[0][0]']     
                                                                                                  
 block5b_add (Add)              (None, 24, 24, 160)  0           ['block5b_drop[0][0]',           
                                                                  'block5a_project_bn[0][0]']     
                                                                                                  
 block5c_expand_conv (Conv2D)   (None, 24, 24, 960)  153600      ['block5b_add[0][0]']            
                                                                                                  
 block5c_expand_bn (BatchNormal  (None, 24, 24, 960)  3840       ['block5c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5c_expand_activation (Act  (None, 24, 24, 960)  0          ['block5c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5c_dwconv (DepthwiseConv2  (None, 24, 24, 960)  24000      ['block5c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5c_bn (BatchNormalization  (None, 24, 24, 960)  3840       ['block5c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5c_activation (Activation  (None, 24, 24, 960)  0          ['block5c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5c_se_squeeze (GlobalAver  (None, 960)         0           ['block5c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5c_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5c_se_squeeze[0][0]']     
                                                                                                  
 block5c_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5c_se_reshape[0][0]']     
                                                                                                  
 block5c_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5c_se_reduce[0][0]']      
                                                                                                  
 block5c_se_excite (Multiply)   (None, 24, 24, 960)  0           ['block5c_activation[0][0]',     
                                                                  'block5c_se_expand[0][0]']      
                                                                                                  
 block5c_project_conv (Conv2D)  (None, 24, 24, 160)  153600      ['block5c_se_excite[0][0]']      
                                                                                                  
 block5c_project_bn (BatchNorma  (None, 24, 24, 160)  640        ['block5c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5c_drop (FixedDropout)    (None, 24, 24, 160)  0           ['block5c_project_bn[0][0]']     
                                                                                                  
 block5c_add (Add)              (None, 24, 24, 160)  0           ['block5c_drop[0][0]',           
                                                                  'block5b_add[0][0]']            
                                                                                                  
 block5d_expand_conv (Conv2D)   (None, 24, 24, 960)  153600      ['block5c_add[0][0]']            
                                                                                                  
 block5d_expand_bn (BatchNormal  (None, 24, 24, 960)  3840       ['block5d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5d_expand_activation (Act  (None, 24, 24, 960)  0          ['block5d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5d_dwconv (DepthwiseConv2  (None, 24, 24, 960)  24000      ['block5d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5d_bn (BatchNormalization  (None, 24, 24, 960)  3840       ['block5d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5d_activation (Activation  (None, 24, 24, 960)  0          ['block5d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5d_se_squeeze (GlobalAver  (None, 960)         0           ['block5d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5d_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5d_se_squeeze[0][0]']     
                                                                                                  
 block5d_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5d_se_reshape[0][0]']     
                                                                                                  
 block5d_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5d_se_reduce[0][0]']      
                                                                                                  
 block5d_se_excite (Multiply)   (None, 24, 24, 960)  0           ['block5d_activation[0][0]',     
                                                                  'block5d_se_expand[0][0]']      
                                                                                                  
 block5d_project_conv (Conv2D)  (None, 24, 24, 160)  153600      ['block5d_se_excite[0][0]']      
                                                                                                  
 block5d_project_bn (BatchNorma  (None, 24, 24, 160)  640        ['block5d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5d_drop (FixedDropout)    (None, 24, 24, 160)  0           ['block5d_project_bn[0][0]']     
                                                                                                  
 block5d_add (Add)              (None, 24, 24, 160)  0           ['block5d_drop[0][0]',           
                                                                  'block5c_add[0][0]']            
                                                                                                  
 block5e_expand_conv (Conv2D)   (None, 24, 24, 960)  153600      ['block5d_add[0][0]']            
                                                                                                  
 block5e_expand_bn (BatchNormal  (None, 24, 24, 960)  3840       ['block5e_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5e_expand_activation (Act  (None, 24, 24, 960)  0          ['block5e_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5e_dwconv (DepthwiseConv2  (None, 24, 24, 960)  24000      ['block5e_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5e_bn (BatchNormalization  (None, 24, 24, 960)  3840       ['block5e_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5e_activation (Activation  (None, 24, 24, 960)  0          ['block5e_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5e_se_squeeze (GlobalAver  (None, 960)         0           ['block5e_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5e_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5e_se_squeeze[0][0]']     
                                                                                                  
 block5e_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5e_se_reshape[0][0]']     
                                                                                                  
 block5e_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5e_se_reduce[0][0]']      
                                                                                                  
 block5e_se_excite (Multiply)   (None, 24, 24, 960)  0           ['block5e_activation[0][0]',     
                                                                  'block5e_se_expand[0][0]']      
                                                                                                  
 block5e_project_conv (Conv2D)  (None, 24, 24, 160)  153600      ['block5e_se_excite[0][0]']      
                                                                                                  
 block5e_project_bn (BatchNorma  (None, 24, 24, 160)  640        ['block5e_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5e_drop (FixedDropout)    (None, 24, 24, 160)  0           ['block5e_project_bn[0][0]']     
                                                                                                  
 block5e_add (Add)              (None, 24, 24, 160)  0           ['block5e_drop[0][0]',           
                                                                  'block5d_add[0][0]']            
                                                                                                  
 block5f_expand_conv (Conv2D)   (None, 24, 24, 960)  153600      ['block5e_add[0][0]']            
                                                                                                  
 block5f_expand_bn (BatchNormal  (None, 24, 24, 960)  3840       ['block5f_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5f_expand_activation (Act  (None, 24, 24, 960)  0          ['block5f_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5f_dwconv (DepthwiseConv2  (None, 24, 24, 960)  24000      ['block5f_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5f_bn (BatchNormalization  (None, 24, 24, 960)  3840       ['block5f_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5f_activation (Activation  (None, 24, 24, 960)  0          ['block5f_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5f_se_squeeze (GlobalAver  (None, 960)         0           ['block5f_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5f_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5f_se_squeeze[0][0]']     
                                                                                                  
 block5f_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5f_se_reshape[0][0]']     
                                                                                                  
 block5f_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5f_se_reduce[0][0]']      
                                                                                                  
 block5f_se_excite (Multiply)   (None, 24, 24, 960)  0           ['block5f_activation[0][0]',     
                                                                  'block5f_se_expand[0][0]']      
                                                                                                  
 block5f_project_conv (Conv2D)  (None, 24, 24, 160)  153600      ['block5f_se_excite[0][0]']      
                                                                                                  
 block5f_project_bn (BatchNorma  (None, 24, 24, 160)  640        ['block5f_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5f_drop (FixedDropout)    (None, 24, 24, 160)  0           ['block5f_project_bn[0][0]']     
                                                                                                  
 block5f_add (Add)              (None, 24, 24, 160)  0           ['block5f_drop[0][0]',           
                                                                  'block5e_add[0][0]']            
                                                                                                  
 block6a_expand_conv (Conv2D)   (None, 24, 24, 960)  153600      ['block5f_add[0][0]']            
                                                                                                  
 block6a_expand_bn (BatchNormal  (None, 24, 24, 960)  3840       ['block6a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6a_expand_activation (Act  (None, 24, 24, 960)  0          ['block6a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6a_dwconv (DepthwiseConv2  (None, 12, 12, 960)  24000      ['block6a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6a_bn (BatchNormalization  (None, 12, 12, 960)  3840       ['block6a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6a_activation (Activation  (None, 12, 12, 960)  0          ['block6a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6a_se_squeeze (GlobalAver  (None, 960)         0           ['block6a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6a_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block6a_se_squeeze[0][0]']     
                                                                                                  
 block6a_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block6a_se_reshape[0][0]']     
                                                                                                  
 block6a_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block6a_se_reduce[0][0]']      
                                                                                                  
 block6a_se_excite (Multiply)   (None, 12, 12, 960)  0           ['block6a_activation[0][0]',     
                                                                  'block6a_se_expand[0][0]']      
                                                                                                  
 block6a_project_conv (Conv2D)  (None, 12, 12, 272)  261120      ['block6a_se_excite[0][0]']      
                                                                                                  
 block6a_project_bn (BatchNorma  (None, 12, 12, 272)  1088       ['block6a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6b_expand_conv (Conv2D)   (None, 12, 12, 1632  443904      ['block6a_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block6b_expand_bn (BatchNormal  (None, 12, 12, 1632  6528       ['block6b_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6b_expand_activation (Act  (None, 12, 12, 1632  0          ['block6b_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6b_dwconv (DepthwiseConv2  (None, 12, 12, 1632  40800      ['block6b_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6b_bn (BatchNormalization  (None, 12, 12, 1632  6528       ['block6b_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6b_activation (Activation  (None, 12, 12, 1632  0          ['block6b_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6b_se_squeeze (GlobalAver  (None, 1632)        0           ['block6b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6b_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6b_se_squeeze[0][0]']     
                                                                                                  
 block6b_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6b_se_reshape[0][0]']     
                                                                                                  
 block6b_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6b_se_reduce[0][0]']      
                                                                                                  
 block6b_se_excite (Multiply)   (None, 12, 12, 1632  0           ['block6b_activation[0][0]',     
                                )                                 'block6b_se_expand[0][0]']      
                                                                                                  
 block6b_project_conv (Conv2D)  (None, 12, 12, 272)  443904      ['block6b_se_excite[0][0]']      
                                                                                                  
 block6b_project_bn (BatchNorma  (None, 12, 12, 272)  1088       ['block6b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6b_drop (FixedDropout)    (None, 12, 12, 272)  0           ['block6b_project_bn[0][0]']     
                                                                                                  
 block6b_add (Add)              (None, 12, 12, 272)  0           ['block6b_drop[0][0]',           
                                                                  'block6a_project_bn[0][0]']     
                                                                                                  
 block6c_expand_conv (Conv2D)   (None, 12, 12, 1632  443904      ['block6b_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6c_expand_bn (BatchNormal  (None, 12, 12, 1632  6528       ['block6c_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6c_expand_activation (Act  (None, 12, 12, 1632  0          ['block6c_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6c_dwconv (DepthwiseConv2  (None, 12, 12, 1632  40800      ['block6c_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6c_bn (BatchNormalization  (None, 12, 12, 1632  6528       ['block6c_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6c_activation (Activation  (None, 12, 12, 1632  0          ['block6c_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6c_se_squeeze (GlobalAver  (None, 1632)        0           ['block6c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6c_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6c_se_squeeze[0][0]']     
                                                                                                  
 block6c_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6c_se_reshape[0][0]']     
                                                                                                  
 block6c_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6c_se_reduce[0][0]']      
                                                                                                  
 block6c_se_excite (Multiply)   (None, 12, 12, 1632  0           ['block6c_activation[0][0]',     
                                )                                 'block6c_se_expand[0][0]']      
                                                                                                  
 block6c_project_conv (Conv2D)  (None, 12, 12, 272)  443904      ['block6c_se_excite[0][0]']      
                                                                                                  
 block6c_project_bn (BatchNorma  (None, 12, 12, 272)  1088       ['block6c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6c_drop (FixedDropout)    (None, 12, 12, 272)  0           ['block6c_project_bn[0][0]']     
                                                                                                  
 block6c_add (Add)              (None, 12, 12, 272)  0           ['block6c_drop[0][0]',           
                                                                  'block6b_add[0][0]']            
                                                                                                  
 block6d_expand_conv (Conv2D)   (None, 12, 12, 1632  443904      ['block6c_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6d_expand_bn (BatchNormal  (None, 12, 12, 1632  6528       ['block6d_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6d_expand_activation (Act  (None, 12, 12, 1632  0          ['block6d_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6d_dwconv (DepthwiseConv2  (None, 12, 12, 1632  40800      ['block6d_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6d_bn (BatchNormalization  (None, 12, 12, 1632  6528       ['block6d_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6d_activation (Activation  (None, 12, 12, 1632  0          ['block6d_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6d_se_squeeze (GlobalAver  (None, 1632)        0           ['block6d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6d_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6d_se_squeeze[0][0]']     
                                                                                                  
 block6d_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6d_se_reshape[0][0]']     
                                                                                                  
 block6d_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6d_se_reduce[0][0]']      
                                                                                                  
 block6d_se_excite (Multiply)   (None, 12, 12, 1632  0           ['block6d_activation[0][0]',     
                                )                                 'block6d_se_expand[0][0]']      
                                                                                                  
 block6d_project_conv (Conv2D)  (None, 12, 12, 272)  443904      ['block6d_se_excite[0][0]']      
                                                                                                  
 block6d_project_bn (BatchNorma  (None, 12, 12, 272)  1088       ['block6d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6d_drop (FixedDropout)    (None, 12, 12, 272)  0           ['block6d_project_bn[0][0]']     
                                                                                                  
 block6d_add (Add)              (None, 12, 12, 272)  0           ['block6d_drop[0][0]',           
                                                                  'block6c_add[0][0]']            
                                                                                                  
 block6e_expand_conv (Conv2D)   (None, 12, 12, 1632  443904      ['block6d_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6e_expand_bn (BatchNormal  (None, 12, 12, 1632  6528       ['block6e_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6e_expand_activation (Act  (None, 12, 12, 1632  0          ['block6e_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6e_dwconv (DepthwiseConv2  (None, 12, 12, 1632  40800      ['block6e_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6e_bn (BatchNormalization  (None, 12, 12, 1632  6528       ['block6e_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6e_activation (Activation  (None, 12, 12, 1632  0          ['block6e_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6e_se_squeeze (GlobalAver  (None, 1632)        0           ['block6e_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6e_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6e_se_squeeze[0][0]']     
                                                                                                  
 block6e_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6e_se_reshape[0][0]']     
                                                                                                  
 block6e_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6e_se_reduce[0][0]']      
                                                                                                  
 block6e_se_excite (Multiply)   (None, 12, 12, 1632  0           ['block6e_activation[0][0]',     
                                )                                 'block6e_se_expand[0][0]']      
                                                                                                  
 block6e_project_conv (Conv2D)  (None, 12, 12, 272)  443904      ['block6e_se_excite[0][0]']      
                                                                                                  
 block6e_project_bn (BatchNorma  (None, 12, 12, 272)  1088       ['block6e_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6e_drop (FixedDropout)    (None, 12, 12, 272)  0           ['block6e_project_bn[0][0]']     
                                                                                                  
 block6e_add (Add)              (None, 12, 12, 272)  0           ['block6e_drop[0][0]',           
                                                                  'block6d_add[0][0]']            
                                                                                                  
 block6f_expand_conv (Conv2D)   (None, 12, 12, 1632  443904      ['block6e_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6f_expand_bn (BatchNormal  (None, 12, 12, 1632  6528       ['block6f_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6f_expand_activation (Act  (None, 12, 12, 1632  0          ['block6f_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6f_dwconv (DepthwiseConv2  (None, 12, 12, 1632  40800      ['block6f_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6f_bn (BatchNormalization  (None, 12, 12, 1632  6528       ['block6f_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6f_activation (Activation  (None, 12, 12, 1632  0          ['block6f_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6f_se_squeeze (GlobalAver  (None, 1632)        0           ['block6f_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6f_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6f_se_squeeze[0][0]']     
                                                                                                  
 block6f_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6f_se_reshape[0][0]']     
                                                                                                  
 block6f_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6f_se_reduce[0][0]']      
                                                                                                  
 block6f_se_excite (Multiply)   (None, 12, 12, 1632  0           ['block6f_activation[0][0]',     
                                )                                 'block6f_se_expand[0][0]']      
                                                                                                  
 block6f_project_conv (Conv2D)  (None, 12, 12, 272)  443904      ['block6f_se_excite[0][0]']      
                                                                                                  
 block6f_project_bn (BatchNorma  (None, 12, 12, 272)  1088       ['block6f_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6f_drop (FixedDropout)    (None, 12, 12, 272)  0           ['block6f_project_bn[0][0]']     
                                                                                                  
 block6f_add (Add)              (None, 12, 12, 272)  0           ['block6f_drop[0][0]',           
                                                                  'block6e_add[0][0]']            
                                                                                                  
 block6g_expand_conv (Conv2D)   (None, 12, 12, 1632  443904      ['block6f_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6g_expand_bn (BatchNormal  (None, 12, 12, 1632  6528       ['block6g_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6g_expand_activation (Act  (None, 12, 12, 1632  0          ['block6g_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6g_dwconv (DepthwiseConv2  (None, 12, 12, 1632  40800      ['block6g_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6g_bn (BatchNormalization  (None, 12, 12, 1632  6528       ['block6g_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6g_activation (Activation  (None, 12, 12, 1632  0          ['block6g_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6g_se_squeeze (GlobalAver  (None, 1632)        0           ['block6g_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6g_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6g_se_squeeze[0][0]']     
                                                                                                  
 block6g_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6g_se_reshape[0][0]']     
                                                                                                  
 block6g_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6g_se_reduce[0][0]']      
                                                                                                  
 block6g_se_excite (Multiply)   (None, 12, 12, 1632  0           ['block6g_activation[0][0]',     
                                )                                 'block6g_se_expand[0][0]']      
                                                                                                  
 block6g_project_conv (Conv2D)  (None, 12, 12, 272)  443904      ['block6g_se_excite[0][0]']      
                                                                                                  
 block6g_project_bn (BatchNorma  (None, 12, 12, 272)  1088       ['block6g_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6g_drop (FixedDropout)    (None, 12, 12, 272)  0           ['block6g_project_bn[0][0]']     
                                                                                                  
 block6g_add (Add)              (None, 12, 12, 272)  0           ['block6g_drop[0][0]',           
                                                                  'block6f_add[0][0]']            
                                                                                                  
 block6h_expand_conv (Conv2D)   (None, 12, 12, 1632  443904      ['block6g_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6h_expand_bn (BatchNormal  (None, 12, 12, 1632  6528       ['block6h_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6h_expand_activation (Act  (None, 12, 12, 1632  0          ['block6h_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6h_dwconv (DepthwiseConv2  (None, 12, 12, 1632  40800      ['block6h_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6h_bn (BatchNormalization  (None, 12, 12, 1632  6528       ['block6h_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6h_activation (Activation  (None, 12, 12, 1632  0          ['block6h_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6h_se_squeeze (GlobalAver  (None, 1632)        0           ['block6h_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6h_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6h_se_squeeze[0][0]']     
                                                                                                  
 block6h_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6h_se_reshape[0][0]']     
                                                                                                  
 block6h_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6h_se_reduce[0][0]']      
                                                                                                  
 block6h_se_excite (Multiply)   (None, 12, 12, 1632  0           ['block6h_activation[0][0]',     
                                )                                 'block6h_se_expand[0][0]']      
                                                                                                  
 block6h_project_conv (Conv2D)  (None, 12, 12, 272)  443904      ['block6h_se_excite[0][0]']      
                                                                                                  
 block6h_project_bn (BatchNorma  (None, 12, 12, 272)  1088       ['block6h_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6h_drop (FixedDropout)    (None, 12, 12, 272)  0           ['block6h_project_bn[0][0]']     
                                                                                                  
 block6h_add (Add)              (None, 12, 12, 272)  0           ['block6h_drop[0][0]',           
                                                                  'block6g_add[0][0]']            
                                                                                                  
 block7a_expand_conv (Conv2D)   (None, 12, 12, 1632  443904      ['block6h_add[0][0]']            
                                )                                                                 
                                                                                                  
 block7a_expand_bn (BatchNormal  (None, 12, 12, 1632  6528       ['block7a_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block7a_expand_activation (Act  (None, 12, 12, 1632  0          ['block7a_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block7a_dwconv (DepthwiseConv2  (None, 12, 12, 1632  14688      ['block7a_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block7a_bn (BatchNormalization  (None, 12, 12, 1632  6528       ['block7a_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block7a_activation (Activation  (None, 12, 12, 1632  0          ['block7a_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block7a_se_squeeze (GlobalAver  (None, 1632)        0           ['block7a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block7a_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block7a_se_squeeze[0][0]']     
                                                                                                  
 block7a_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block7a_se_reshape[0][0]']     
                                                                                                  
 block7a_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block7a_se_reduce[0][0]']      
                                                                                                  
 block7a_se_excite (Multiply)   (None, 12, 12, 1632  0           ['block7a_activation[0][0]',     
                                )                                 'block7a_se_expand[0][0]']      
                                                                                                  
 block7a_project_conv (Conv2D)  (None, 12, 12, 448)  731136      ['block7a_se_excite[0][0]']      
                                                                                                  
 block7a_project_bn (BatchNorma  (None, 12, 12, 448)  1792       ['block7a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block7b_expand_conv (Conv2D)   (None, 12, 12, 2688  1204224     ['block7a_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block7b_expand_bn (BatchNormal  (None, 12, 12, 2688  10752      ['block7b_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block7b_expand_activation (Act  (None, 12, 12, 2688  0          ['block7b_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block7b_dwconv (DepthwiseConv2  (None, 12, 12, 2688  24192      ['block7b_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block7b_bn (BatchNormalization  (None, 12, 12, 2688  10752      ['block7b_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block7b_activation (Activation  (None, 12, 12, 2688  0          ['block7b_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block7b_se_squeeze (GlobalAver  (None, 2688)        0           ['block7b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block7b_se_reshape (Reshape)   (None, 1, 1, 2688)   0           ['block7b_se_squeeze[0][0]']     
                                                                                                  
 block7b_se_reduce (Conv2D)     (None, 1, 1, 112)    301168      ['block7b_se_reshape[0][0]']     
                                                                                                  
 block7b_se_expand (Conv2D)     (None, 1, 1, 2688)   303744      ['block7b_se_reduce[0][0]']      
                                                                                                  
 block7b_se_excite (Multiply)   (None, 12, 12, 2688  0           ['block7b_activation[0][0]',     
                                )                                 'block7b_se_expand[0][0]']      
                                                                                                  
 block7b_project_conv (Conv2D)  (None, 12, 12, 448)  1204224     ['block7b_se_excite[0][0]']      
                                                                                                  
 block7b_project_bn (BatchNorma  (None, 12, 12, 448)  1792       ['block7b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block7b_drop (FixedDropout)    (None, 12, 12, 448)  0           ['block7b_project_bn[0][0]']     
                                                                                                  
 block7b_add (Add)              (None, 12, 12, 448)  0           ['block7b_drop[0][0]',           
                                                                  'block7a_project_bn[0][0]']     
                                                                                                  
 top_conv (Conv2D)              (None, 12, 12, 1792  802816      ['block7b_add[0][0]']            
                                )                                                                 
                                                                                                  
 top_bn (BatchNormalization)    (None, 12, 12, 1792  7168        ['top_conv[0][0]']               
                                )                                                                 
                                                                                                  
 top_activation (Activation)    (None, 12, 12, 1792  0           ['top_bn[0][0]']                 
                                )                                                                 
                                                                                                  
 flatten_4 (Flatten)            (None, 258048)       0           ['top_activation[0][0]']         
                                                                                                  
 dense_8 (Dense)                (None, 1024)         264242176   ['flatten_4[0][0]']              
                                                                                                  
 dropout_4 (Dropout)            (None, 1024)         0           ['dense_8[0][0]']                
                                                                                                  
 dense_9 (Dense)                (None, 1)            1025        ['dropout_4[0][0]']              
                                                                                                  
==================================================================================================
Total params: 281,917,017
Trainable params: 264,243,201
Non-trainable params: 17,673,816
__________________________________________________________________________________________________

#get total parameters
model_params = model_final.count_params()

# Specify the optimizer, loss function and evaluation metrics. 
model_final.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001), metrics=['accuracy'])

t1 = time.time()
#train the model
eff_history = model_final.fit_generator(train_generator, validation_data = validation_generator, steps_per_epoch = 100, epochs = 10)
fit_time = time.time() - t1

<ipython-input-104-b7f31b017b18>:3: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  eff_history = model_final.fit_generator(train_generator, validation_data = validation_generator, steps_per_epoch = 100, epochs = 10)

Epoch 1/10
100/100 [==============================] - 93s 805ms/step - loss: 0.6515 - accuracy: 0.9295 - val_loss: 0.0976 - val_accuracy: 0.9887
Epoch 2/10
100/100 [==============================] - 76s 754ms/step - loss: 0.5932 - accuracy: 0.9555 - val_loss: 0.0481 - val_accuracy: 0.9937
Epoch 3/10
100/100 [==============================] - 76s 756ms/step - loss: 0.3212 - accuracy: 0.9673 - val_loss: 0.0374 - val_accuracy: 0.9950
Epoch 4/10
100/100 [==============================] - 77s 767ms/step - loss: 0.4213 - accuracy: 0.9645 - val_loss: 0.0330 - val_accuracy: 0.9912
Epoch 5/10
100/100 [==============================] - 76s 763ms/step - loss: 0.4679 - accuracy: 0.9675 - val_loss: 0.0667 - val_accuracy: 0.9912
Epoch 6/10
100/100 [==============================] - 77s 769ms/step - loss: 0.6451 - accuracy: 0.9685 - val_loss: 0.0410 - val_accuracy: 0.9937
Epoch 7/10
100/100 [==============================] - 76s 760ms/step - loss: 0.7479 - accuracy: 0.9602 - val_loss: 0.0122 - val_accuracy: 0.9962
Epoch 8/10
100/100 [==============================] - 76s 763ms/step - loss: 0.4988 - accuracy: 0.9733 - val_loss: 0.0065 - val_accuracy: 0.9975
Epoch 9/10
100/100 [==============================] - 77s 772ms/step - loss: 0.5127 - accuracy: 0.9715 - val_loss: 0.0069 - val_accuracy: 0.9975
Epoch 10/10
100/100 [==============================] - 76s 761ms/step - loss: 0.4433 - accuracy: 0.9725 - val_loss: 0.0030 - val_accuracy: 0.9987

# time it took to fit the model
print(fit_time) 

787.6704769134521

#Plot training and validation accuracy and loss for each epoch
acc = eff_history.history['accuracy']
val_acc = eff_history.history['val_accuracy']
loss = eff_history.history['loss']
val_loss = eff_history.history['val_loss']

epochs = range(1,len(acc) + 1)

plt.plot(epochs,acc,label = 'Training Accuracy')
plt.plot(epochs,val_acc,label = 'Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()
plt.figure()

plt.plot(epochs,loss,label = 'Training loss')
plt.plot(epochs,val_loss,label = 'Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()

plt.show()

# Test dataset 
test_datagen = ImageDataGenerator(rescale=1./255)

test_generator = test_datagen.flow_from_directory(
        test_dir,
        target_size=(380, 380),
        shuffle = False,
        class_mode='binary',
        batch_size=1)

Found 2023 images belonging to 2 classes.

#Get test length
filenames = test_generator.filenames
nb_samples = len(filenames)

#Predict on test set
predict = model_final.predict_generator(test_generator,steps = nb_samples)

<ipython-input-109-7710eff794cf>:2: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.
  predict = model_final.predict_generator(test_generator,steps = nb_samples)

#Get list of prediction results
pred_list = []
for i in predict:
    if i > 0.5:
        result = 1 #dog
        pred_list.append(result)
    else:
      result = 0 #cat
      pred_list.append(result)

#Create dataframe of image ID, image true label, image predicted label
import pandas as pd
image_ids = [name.split('/')[-1] for name in test_generator.filenames]
image_label = [name.split('/')[0] for name in test_generator.filenames]
data = {'id': image_ids, 'label':image_label, 'prediction':pred_list} 
data_df = pd.DataFrame(data)
data_df.label.replace(('cats', 'dogs'), (0, 1), inplace=True) # change cat and dog label to 0 or 1

#Get test accuracy score
from sklearn.metrics import accuracy_score, confusion_matrix
test_accuracy = accuracy_score(data_df['label'], data_df['prediction'])
print('Test Accuracy: ', round((test_accuracy * 100), 2), "%")

Test Accuracy:  98.71 %

from sklearn.metrics import classification_report
#Classification Report
print(classification_report(data_df['label'], data_df['prediction']))

              precision    recall  f1-score   support

           0       1.00      0.98      0.99      1011
           1       0.98      1.00      0.99      1012

    accuracy                           0.99      2023
   macro avg       0.99      0.99      0.99      2023
weighted avg       0.99      0.99      0.99      2023

#Create confusion matrix
import seaborn as sns
label = [0, 1] #0 = cat and 1 = dog
cm = confusion_matrix(data_df['label'], data_df['prediction'], labels = label)
#Plot
ax= plt.subplot()
sns.heatmap(cm, annot=True, fmt='g', ax=ax);
# labels, title and ticks
ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); 
ax.set_title('Confusion Matrix'); 
ax.xaxis.set_ticklabels(["Cat", "Dog"]); ax.yaxis.set_ticklabels(["Cat", "Dog"])

[Text(0, 0.5, 'Cat'), Text(0, 1.5, 'Dog')]

ExperimentLog.loc[len(ExperimentLog)] = [
    "EfficientNet B4",
    380,
    "RMSprop", 
    10, 
    max(acc), 
    max(val_acc),
    test_accuracy,
    fit_time,
    model_params
]

ExperimentLog 

	Base Model	Input Resolution	Optimizer	Epochs	Training Accuracy	Validation Accuracy	Test Accuracy	Fit Time	Total Parameters
0	EfficientNet B0	224	RMSprop	10	0.951500	0.98750	0.971330	2195.619411	68276893
1	EfficientNet B0 with decay	224	RMSprop	10	0.956171	0.98750	0.974790	381.655278	68276893
2	EfficientNet B1	240	RMSprop	10	0.959698	0.99125	0.985171	349.486195	90463361
3	EfficientNet B2	260	RMSprop	10	0.965239	0.99500	0.983193	380.205944	124555763
4	EfficientNet B3	300	RMSprop	10	0.970277	0.99750	0.988631	516.328792	168071977
5	EfficientNet B4	380	RMSprop	10	0.973300	0.99875	0.987148	787.670477	281917017

EfficientNetB5

# Add rescaling and augmentation to ImageDataGenerator for the training set
train_datagen = ImageDataGenerator(rescale = 1./255., rotation_range = 40, width_shift_range = 0.2, height_shift_range = 0.2, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True, validation_split=0.1) # set validation split
# Rescale validation set. No augmentation on the validation set. 
validation_datagen = ImageDataGenerator(rescale = 1./255.,validation_split=0.1) # set validation split
#Read images directly from directory. 
train_generator = train_datagen.flow_from_directory(train_dir, seed = 42, shuffle = True, batch_size = 20, class_mode = 'binary', target_size = (456, 456), subset='training') #set as training data
validation_generator = validation_datagen.flow_from_directory(train_dir, seed = 42, shuffle = True, batch_size = 20, class_mode = 'binary', target_size = (456, 456), subset='validation') # same directory as training data. Set as validation data

Found 7205 images belonging to 2 classes.
Found 800 images belonging to 2 classes.

#Instantiates the EfficientNet architecture
base_model = efn.EfficientNetB5(input_shape = (456, 456, 3), include_top = False, weights = 'imagenet')

Downloading data from https://github.com/Callidior/keras-applications/releases/download/efficientnet/efficientnet-b5_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5
115515256/115515256 [==============================] - 15s 0us/step

# Set trainable attribute to false for all of the base model layers
for layer in base_model.layers:
    layer.trainable = False

#Build on top of existing base model. 
x = base_model.output
x = layers.Flatten()(x) #convert to 1D array
x = layers.Dense(1024, activation="relu")(x) #fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dropout(0.5)(x) #Drops 50% of inputs to zero at each training iteration (prevents overfitting)

# Add a final sigmoid layer with 1 node for classification output (probability between 0 and 1)
predictions = layers.Dense(1, activation="sigmoid")(x)
model_final = Model(inputs = base_model.input, outputs = predictions)

#Print model summary
from torchsummary import summary
model_sum = model_final.summary()

Model: "model_5"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_6 (InputLayer)           [(None, 456, 456, 3  0           []                               
                                )]                                                                
                                                                                                  
 stem_conv (Conv2D)             (None, 228, 228, 48  1296        ['input_6[0][0]']                
                                )                                                                 
                                                                                                  
 stem_bn (BatchNormalization)   (None, 228, 228, 48  192         ['stem_conv[0][0]']              
                                )                                                                 
                                                                                                  
 stem_activation (Activation)   (None, 228, 228, 48  0           ['stem_bn[0][0]']                
                                )                                                                 
                                                                                                  
 block1a_dwconv (DepthwiseConv2  (None, 228, 228, 48  432        ['stem_activation[0][0]']        
 D)                             )                                                                 
                                                                                                  
 block1a_bn (BatchNormalization  (None, 228, 228, 48  192        ['block1a_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block1a_activation (Activation  (None, 228, 228, 48  0          ['block1a_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block1a_se_squeeze (GlobalAver  (None, 48)          0           ['block1a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block1a_se_reshape (Reshape)   (None, 1, 1, 48)     0           ['block1a_se_squeeze[0][0]']     
                                                                                                  
 block1a_se_reduce (Conv2D)     (None, 1, 1, 12)     588         ['block1a_se_reshape[0][0]']     
                                                                                                  
 block1a_se_expand (Conv2D)     (None, 1, 1, 48)     624         ['block1a_se_reduce[0][0]']      
                                                                                                  
 block1a_se_excite (Multiply)   (None, 228, 228, 48  0           ['block1a_activation[0][0]',     
                                )                                 'block1a_se_expand[0][0]']      
                                                                                                  
 block1a_project_conv (Conv2D)  (None, 228, 228, 24  1152        ['block1a_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block1a_project_bn (BatchNorma  (None, 228, 228, 24  96         ['block1a_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block1b_dwconv (DepthwiseConv2  (None, 228, 228, 24  216        ['block1a_project_bn[0][0]']     
 D)                             )                                                                 
                                                                                                  
 block1b_bn (BatchNormalization  (None, 228, 228, 24  96         ['block1b_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block1b_activation (Activation  (None, 228, 228, 24  0          ['block1b_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block1b_se_squeeze (GlobalAver  (None, 24)          0           ['block1b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block1b_se_reshape (Reshape)   (None, 1, 1, 24)     0           ['block1b_se_squeeze[0][0]']     
                                                                                                  
 block1b_se_reduce (Conv2D)     (None, 1, 1, 6)      150         ['block1b_se_reshape[0][0]']     
                                                                                                  
 block1b_se_expand (Conv2D)     (None, 1, 1, 24)     168         ['block1b_se_reduce[0][0]']      
                                                                                                  
 block1b_se_excite (Multiply)   (None, 228, 228, 24  0           ['block1b_activation[0][0]',     
                                )                                 'block1b_se_expand[0][0]']      
                                                                                                  
 block1b_project_conv (Conv2D)  (None, 228, 228, 24  576         ['block1b_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block1b_project_bn (BatchNorma  (None, 228, 228, 24  96         ['block1b_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block1b_drop (FixedDropout)    (None, 228, 228, 24  0           ['block1b_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block1b_add (Add)              (None, 228, 228, 24  0           ['block1b_drop[0][0]',           
                                )                                 'block1a_project_bn[0][0]']     
                                                                                                  
 block1c_dwconv (DepthwiseConv2  (None, 228, 228, 24  216        ['block1b_add[0][0]']            
 D)                             )                                                                 
                                                                                                  
 block1c_bn (BatchNormalization  (None, 228, 228, 24  96         ['block1c_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block1c_activation (Activation  (None, 228, 228, 24  0          ['block1c_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block1c_se_squeeze (GlobalAver  (None, 24)          0           ['block1c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block1c_se_reshape (Reshape)   (None, 1, 1, 24)     0           ['block1c_se_squeeze[0][0]']     
                                                                                                  
 block1c_se_reduce (Conv2D)     (None, 1, 1, 6)      150         ['block1c_se_reshape[0][0]']     
                                                                                                  
 block1c_se_expand (Conv2D)     (None, 1, 1, 24)     168         ['block1c_se_reduce[0][0]']      
                                                                                                  
 block1c_se_excite (Multiply)   (None, 228, 228, 24  0           ['block1c_activation[0][0]',     
                                )                                 'block1c_se_expand[0][0]']      
                                                                                                  
 block1c_project_conv (Conv2D)  (None, 228, 228, 24  576         ['block1c_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block1c_project_bn (BatchNorma  (None, 228, 228, 24  96         ['block1c_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block1c_drop (FixedDropout)    (None, 228, 228, 24  0           ['block1c_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block1c_add (Add)              (None, 228, 228, 24  0           ['block1c_drop[0][0]',           
                                )                                 'block1b_add[0][0]']            
                                                                                                  
 block2a_expand_conv (Conv2D)   (None, 228, 228, 14  3456        ['block1c_add[0][0]']            
                                4)                                                                
                                                                                                  
 block2a_expand_bn (BatchNormal  (None, 228, 228, 14  576        ['block2a_expand_conv[0][0]']    
 ization)                       4)                                                                
                                                                                                  
 block2a_expand_activation (Act  (None, 228, 228, 14  0          ['block2a_expand_bn[0][0]']      
 ivation)                       4)                                                                
                                                                                                  
 block2a_dwconv (DepthwiseConv2  (None, 114, 114, 14  1296       ['block2a_expand_activation[0][0]
 D)                             4)                               ']                               
                                                                                                  
 block2a_bn (BatchNormalization  (None, 114, 114, 14  576        ['block2a_dwconv[0][0]']         
 )                              4)                                                                
                                                                                                  
 block2a_activation (Activation  (None, 114, 114, 14  0          ['block2a_bn[0][0]']             
 )                              4)                                                                
                                                                                                  
 block2a_se_squeeze (GlobalAver  (None, 144)         0           ['block2a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2a_se_squeeze[0][0]']     
                                                                                                  
 block2a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2a_se_reshape[0][0]']     
                                                                                                  
 block2a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2a_se_reduce[0][0]']      
                                                                                                  
 block2a_se_excite (Multiply)   (None, 114, 114, 14  0           ['block2a_activation[0][0]',     
                                4)                                'block2a_se_expand[0][0]']      
                                                                                                  
 block2a_project_conv (Conv2D)  (None, 114, 114, 40  5760        ['block2a_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block2a_project_bn (BatchNorma  (None, 114, 114, 40  160        ['block2a_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block2b_expand_conv (Conv2D)   (None, 114, 114, 24  9600        ['block2a_project_bn[0][0]']     
                                0)                                                                
                                                                                                  
 block2b_expand_bn (BatchNormal  (None, 114, 114, 24  960        ['block2b_expand_conv[0][0]']    
 ization)                       0)                                                                
                                                                                                  
 block2b_expand_activation (Act  (None, 114, 114, 24  0          ['block2b_expand_bn[0][0]']      
 ivation)                       0)                                                                
                                                                                                  
 block2b_dwconv (DepthwiseConv2  (None, 114, 114, 24  2160       ['block2b_expand_activation[0][0]
 D)                             0)                               ']                               
                                                                                                  
 block2b_bn (BatchNormalization  (None, 114, 114, 24  960        ['block2b_dwconv[0][0]']         
 )                              0)                                                                
                                                                                                  
 block2b_activation (Activation  (None, 114, 114, 24  0          ['block2b_bn[0][0]']             
 )                              0)                                                                
                                                                                                  
 block2b_se_squeeze (GlobalAver  (None, 240)         0           ['block2b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2b_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block2b_se_squeeze[0][0]']     
                                                                                                  
 block2b_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block2b_se_reshape[0][0]']     
                                                                                                  
 block2b_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block2b_se_reduce[0][0]']      
                                                                                                  
 block2b_se_excite (Multiply)   (None, 114, 114, 24  0           ['block2b_activation[0][0]',     
                                0)                                'block2b_se_expand[0][0]']      
                                                                                                  
 block2b_project_conv (Conv2D)  (None, 114, 114, 40  9600        ['block2b_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block2b_project_bn (BatchNorma  (None, 114, 114, 40  160        ['block2b_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block2b_drop (FixedDropout)    (None, 114, 114, 40  0           ['block2b_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block2b_add (Add)              (None, 114, 114, 40  0           ['block2b_drop[0][0]',           
                                )                                 'block2a_project_bn[0][0]']     
                                                                                                  
 block2c_expand_conv (Conv2D)   (None, 114, 114, 24  9600        ['block2b_add[0][0]']            
                                0)                                                                
                                                                                                  
 block2c_expand_bn (BatchNormal  (None, 114, 114, 24  960        ['block2c_expand_conv[0][0]']    
 ization)                       0)                                                                
                                                                                                  
 block2c_expand_activation (Act  (None, 114, 114, 24  0          ['block2c_expand_bn[0][0]']      
 ivation)                       0)                                                                
                                                                                                  
 block2c_dwconv (DepthwiseConv2  (None, 114, 114, 24  2160       ['block2c_expand_activation[0][0]
 D)                             0)                               ']                               
                                                                                                  
 block2c_bn (BatchNormalization  (None, 114, 114, 24  960        ['block2c_dwconv[0][0]']         
 )                              0)                                                                
                                                                                                  
 block2c_activation (Activation  (None, 114, 114, 24  0          ['block2c_bn[0][0]']             
 )                              0)                                                                
                                                                                                  
 block2c_se_squeeze (GlobalAver  (None, 240)         0           ['block2c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2c_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block2c_se_squeeze[0][0]']     
                                                                                                  
 block2c_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block2c_se_reshape[0][0]']     
                                                                                                  
 block2c_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block2c_se_reduce[0][0]']      
                                                                                                  
 block2c_se_excite (Multiply)   (None, 114, 114, 24  0           ['block2c_activation[0][0]',     
                                0)                                'block2c_se_expand[0][0]']      
                                                                                                  
 block2c_project_conv (Conv2D)  (None, 114, 114, 40  9600        ['block2c_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block2c_project_bn (BatchNorma  (None, 114, 114, 40  160        ['block2c_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block2c_drop (FixedDropout)    (None, 114, 114, 40  0           ['block2c_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block2c_add (Add)              (None, 114, 114, 40  0           ['block2c_drop[0][0]',           
                                )                                 'block2b_add[0][0]']            
                                                                                                  
 block2d_expand_conv (Conv2D)   (None, 114, 114, 24  9600        ['block2c_add[0][0]']            
                                0)                                                                
                                                                                                  
 block2d_expand_bn (BatchNormal  (None, 114, 114, 24  960        ['block2d_expand_conv[0][0]']    
 ization)                       0)                                                                
                                                                                                  
 block2d_expand_activation (Act  (None, 114, 114, 24  0          ['block2d_expand_bn[0][0]']      
 ivation)                       0)                                                                
                                                                                                  
 block2d_dwconv (DepthwiseConv2  (None, 114, 114, 24  2160       ['block2d_expand_activation[0][0]
 D)                             0)                               ']                               
                                                                                                  
 block2d_bn (BatchNormalization  (None, 114, 114, 24  960        ['block2d_dwconv[0][0]']         
 )                              0)                                                                
                                                                                                  
 block2d_activation (Activation  (None, 114, 114, 24  0          ['block2d_bn[0][0]']             
 )                              0)                                                                
                                                                                                  
 block2d_se_squeeze (GlobalAver  (None, 240)         0           ['block2d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2d_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block2d_se_squeeze[0][0]']     
                                                                                                  
 block2d_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block2d_se_reshape[0][0]']     
                                                                                                  
 block2d_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block2d_se_reduce[0][0]']      
                                                                                                  
 block2d_se_excite (Multiply)   (None, 114, 114, 24  0           ['block2d_activation[0][0]',     
                                0)                                'block2d_se_expand[0][0]']      
                                                                                                  
 block2d_project_conv (Conv2D)  (None, 114, 114, 40  9600        ['block2d_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block2d_project_bn (BatchNorma  (None, 114, 114, 40  160        ['block2d_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block2d_drop (FixedDropout)    (None, 114, 114, 40  0           ['block2d_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block2d_add (Add)              (None, 114, 114, 40  0           ['block2d_drop[0][0]',           
                                )                                 'block2c_add[0][0]']            
                                                                                                  
 block2e_expand_conv (Conv2D)   (None, 114, 114, 24  9600        ['block2d_add[0][0]']            
                                0)                                                                
                                                                                                  
 block2e_expand_bn (BatchNormal  (None, 114, 114, 24  960        ['block2e_expand_conv[0][0]']    
 ization)                       0)                                                                
                                                                                                  
 block2e_expand_activation (Act  (None, 114, 114, 24  0          ['block2e_expand_bn[0][0]']      
 ivation)                       0)                                                                
                                                                                                  
 block2e_dwconv (DepthwiseConv2  (None, 114, 114, 24  2160       ['block2e_expand_activation[0][0]
 D)                             0)                               ']                               
                                                                                                  
 block2e_bn (BatchNormalization  (None, 114, 114, 24  960        ['block2e_dwconv[0][0]']         
 )                              0)                                                                
                                                                                                  
 block2e_activation (Activation  (None, 114, 114, 24  0          ['block2e_bn[0][0]']             
 )                              0)                                                                
                                                                                                  
 block2e_se_squeeze (GlobalAver  (None, 240)         0           ['block2e_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2e_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block2e_se_squeeze[0][0]']     
                                                                                                  
 block2e_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block2e_se_reshape[0][0]']     
                                                                                                  
 block2e_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block2e_se_reduce[0][0]']      
                                                                                                  
 block2e_se_excite (Multiply)   (None, 114, 114, 24  0           ['block2e_activation[0][0]',     
                                0)                                'block2e_se_expand[0][0]']      
                                                                                                  
 block2e_project_conv (Conv2D)  (None, 114, 114, 40  9600        ['block2e_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block2e_project_bn (BatchNorma  (None, 114, 114, 40  160        ['block2e_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block2e_drop (FixedDropout)    (None, 114, 114, 40  0           ['block2e_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block2e_add (Add)              (None, 114, 114, 40  0           ['block2e_drop[0][0]',           
                                )                                 'block2d_add[0][0]']            
                                                                                                  
 block3a_expand_conv (Conv2D)   (None, 114, 114, 24  9600        ['block2e_add[0][0]']            
                                0)                                                                
                                                                                                  
 block3a_expand_bn (BatchNormal  (None, 114, 114, 24  960        ['block3a_expand_conv[0][0]']    
 ization)                       0)                                                                
                                                                                                  
 block3a_expand_activation (Act  (None, 114, 114, 24  0          ['block3a_expand_bn[0][0]']      
 ivation)                       0)                                                                
                                                                                                  
 block3a_dwconv (DepthwiseConv2  (None, 57, 57, 240)  6000       ['block3a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3a_bn (BatchNormalization  (None, 57, 57, 240)  960        ['block3a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3a_activation (Activation  (None, 57, 57, 240)  0          ['block3a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3a_se_squeeze (GlobalAver  (None, 240)         0           ['block3a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3a_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block3a_se_squeeze[0][0]']     
                                                                                                  
 block3a_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block3a_se_reshape[0][0]']     
                                                                                                  
 block3a_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block3a_se_reduce[0][0]']      
                                                                                                  
 block3a_se_excite (Multiply)   (None, 57, 57, 240)  0           ['block3a_activation[0][0]',     
                                                                  'block3a_se_expand[0][0]']      
                                                                                                  
 block3a_project_conv (Conv2D)  (None, 57, 57, 64)   15360       ['block3a_se_excite[0][0]']      
                                                                                                  
 block3a_project_bn (BatchNorma  (None, 57, 57, 64)  256         ['block3a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3b_expand_conv (Conv2D)   (None, 57, 57, 384)  24576       ['block3a_project_bn[0][0]']     
                                                                                                  
 block3b_expand_bn (BatchNormal  (None, 57, 57, 384)  1536       ['block3b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3b_expand_activation (Act  (None, 57, 57, 384)  0          ['block3b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3b_dwconv (DepthwiseConv2  (None, 57, 57, 384)  9600       ['block3b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3b_bn (BatchNormalization  (None, 57, 57, 384)  1536       ['block3b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3b_activation (Activation  (None, 57, 57, 384)  0          ['block3b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3b_se_squeeze (GlobalAver  (None, 384)         0           ['block3b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3b_se_reshape (Reshape)   (None, 1, 1, 384)    0           ['block3b_se_squeeze[0][0]']     
                                                                                                  
 block3b_se_reduce (Conv2D)     (None, 1, 1, 16)     6160        ['block3b_se_reshape[0][0]']     
                                                                                                  
 block3b_se_expand (Conv2D)     (None, 1, 1, 384)    6528        ['block3b_se_reduce[0][0]']      
                                                                                                  
 block3b_se_excite (Multiply)   (None, 57, 57, 384)  0           ['block3b_activation[0][0]',     
                                                                  'block3b_se_expand[0][0]']      
                                                                                                  
 block3b_project_conv (Conv2D)  (None, 57, 57, 64)   24576       ['block3b_se_excite[0][0]']      
                                                                                                  
 block3b_project_bn (BatchNorma  (None, 57, 57, 64)  256         ['block3b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3b_drop (FixedDropout)    (None, 57, 57, 64)   0           ['block3b_project_bn[0][0]']     
                                                                                                  
 block3b_add (Add)              (None, 57, 57, 64)   0           ['block3b_drop[0][0]',           
                                                                  'block3a_project_bn[0][0]']     
                                                                                                  
 block3c_expand_conv (Conv2D)   (None, 57, 57, 384)  24576       ['block3b_add[0][0]']            
                                                                                                  
 block3c_expand_bn (BatchNormal  (None, 57, 57, 384)  1536       ['block3c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3c_expand_activation (Act  (None, 57, 57, 384)  0          ['block3c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3c_dwconv (DepthwiseConv2  (None, 57, 57, 384)  9600       ['block3c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3c_bn (BatchNormalization  (None, 57, 57, 384)  1536       ['block3c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3c_activation (Activation  (None, 57, 57, 384)  0          ['block3c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3c_se_squeeze (GlobalAver  (None, 384)         0           ['block3c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3c_se_reshape (Reshape)   (None, 1, 1, 384)    0           ['block3c_se_squeeze[0][0]']     
                                                                                                  
 block3c_se_reduce (Conv2D)     (None, 1, 1, 16)     6160        ['block3c_se_reshape[0][0]']     
                                                                                                  
 block3c_se_expand (Conv2D)     (None, 1, 1, 384)    6528        ['block3c_se_reduce[0][0]']      
                                                                                                  
 block3c_se_excite (Multiply)   (None, 57, 57, 384)  0           ['block3c_activation[0][0]',     
                                                                  'block3c_se_expand[0][0]']      
                                                                                                  
 block3c_project_conv (Conv2D)  (None, 57, 57, 64)   24576       ['block3c_se_excite[0][0]']      
                                                                                                  
 block3c_project_bn (BatchNorma  (None, 57, 57, 64)  256         ['block3c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3c_drop (FixedDropout)    (None, 57, 57, 64)   0           ['block3c_project_bn[0][0]']     
                                                                                                  
 block3c_add (Add)              (None, 57, 57, 64)   0           ['block3c_drop[0][0]',           
                                                                  'block3b_add[0][0]']            
                                                                                                  
 block3d_expand_conv (Conv2D)   (None, 57, 57, 384)  24576       ['block3c_add[0][0]']            
                                                                                                  
 block3d_expand_bn (BatchNormal  (None, 57, 57, 384)  1536       ['block3d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3d_expand_activation (Act  (None, 57, 57, 384)  0          ['block3d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3d_dwconv (DepthwiseConv2  (None, 57, 57, 384)  9600       ['block3d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3d_bn (BatchNormalization  (None, 57, 57, 384)  1536       ['block3d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3d_activation (Activation  (None, 57, 57, 384)  0          ['block3d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3d_se_squeeze (GlobalAver  (None, 384)         0           ['block3d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3d_se_reshape (Reshape)   (None, 1, 1, 384)    0           ['block3d_se_squeeze[0][0]']     
                                                                                                  
 block3d_se_reduce (Conv2D)     (None, 1, 1, 16)     6160        ['block3d_se_reshape[0][0]']     
                                                                                                  
 block3d_se_expand (Conv2D)     (None, 1, 1, 384)    6528        ['block3d_se_reduce[0][0]']      
                                                                                                  
 block3d_se_excite (Multiply)   (None, 57, 57, 384)  0           ['block3d_activation[0][0]',     
                                                                  'block3d_se_expand[0][0]']      
                                                                                                  
 block3d_project_conv (Conv2D)  (None, 57, 57, 64)   24576       ['block3d_se_excite[0][0]']      
                                                                                                  
 block3d_project_bn (BatchNorma  (None, 57, 57, 64)  256         ['block3d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3d_drop (FixedDropout)    (None, 57, 57, 64)   0           ['block3d_project_bn[0][0]']     
                                                                                                  
 block3d_add (Add)              (None, 57, 57, 64)   0           ['block3d_drop[0][0]',           
                                                                  'block3c_add[0][0]']            
                                                                                                  
 block3e_expand_conv (Conv2D)   (None, 57, 57, 384)  24576       ['block3d_add[0][0]']            
                                                                                                  
 block3e_expand_bn (BatchNormal  (None, 57, 57, 384)  1536       ['block3e_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3e_expand_activation (Act  (None, 57, 57, 384)  0          ['block3e_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3e_dwconv (DepthwiseConv2  (None, 57, 57, 384)  9600       ['block3e_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3e_bn (BatchNormalization  (None, 57, 57, 384)  1536       ['block3e_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3e_activation (Activation  (None, 57, 57, 384)  0          ['block3e_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3e_se_squeeze (GlobalAver  (None, 384)         0           ['block3e_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3e_se_reshape (Reshape)   (None, 1, 1, 384)    0           ['block3e_se_squeeze[0][0]']     
                                                                                                  
 block3e_se_reduce (Conv2D)     (None, 1, 1, 16)     6160        ['block3e_se_reshape[0][0]']     
                                                                                                  
 block3e_se_expand (Conv2D)     (None, 1, 1, 384)    6528        ['block3e_se_reduce[0][0]']      
                                                                                                  
 block3e_se_excite (Multiply)   (None, 57, 57, 384)  0           ['block3e_activation[0][0]',     
                                                                  'block3e_se_expand[0][0]']      
                                                                                                  
 block3e_project_conv (Conv2D)  (None, 57, 57, 64)   24576       ['block3e_se_excite[0][0]']      
                                                                                                  
 block3e_project_bn (BatchNorma  (None, 57, 57, 64)  256         ['block3e_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3e_drop (FixedDropout)    (None, 57, 57, 64)   0           ['block3e_project_bn[0][0]']     
                                                                                                  
 block3e_add (Add)              (None, 57, 57, 64)   0           ['block3e_drop[0][0]',           
                                                                  'block3d_add[0][0]']            
                                                                                                  
 block4a_expand_conv (Conv2D)   (None, 57, 57, 384)  24576       ['block3e_add[0][0]']            
                                                                                                  
 block4a_expand_bn (BatchNormal  (None, 57, 57, 384)  1536       ['block4a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4a_expand_activation (Act  (None, 57, 57, 384)  0          ['block4a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4a_dwconv (DepthwiseConv2  (None, 29, 29, 384)  3456       ['block4a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4a_bn (BatchNormalization  (None, 29, 29, 384)  1536       ['block4a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4a_activation (Activation  (None, 29, 29, 384)  0          ['block4a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4a_se_squeeze (GlobalAver  (None, 384)         0           ['block4a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4a_se_reshape (Reshape)   (None, 1, 1, 384)    0           ['block4a_se_squeeze[0][0]']     
                                                                                                  
 block4a_se_reduce (Conv2D)     (None, 1, 1, 16)     6160        ['block4a_se_reshape[0][0]']     
                                                                                                  
 block4a_se_expand (Conv2D)     (None, 1, 1, 384)    6528        ['block4a_se_reduce[0][0]']      
                                                                                                  
 block4a_se_excite (Multiply)   (None, 29, 29, 384)  0           ['block4a_activation[0][0]',     
                                                                  'block4a_se_expand[0][0]']      
                                                                                                  
 block4a_project_conv (Conv2D)  (None, 29, 29, 128)  49152       ['block4a_se_excite[0][0]']      
                                                                                                  
 block4a_project_bn (BatchNorma  (None, 29, 29, 128)  512        ['block4a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4b_expand_conv (Conv2D)   (None, 29, 29, 768)  98304       ['block4a_project_bn[0][0]']     
                                                                                                  
 block4b_expand_bn (BatchNormal  (None, 29, 29, 768)  3072       ['block4b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4b_expand_activation (Act  (None, 29, 29, 768)  0          ['block4b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4b_dwconv (DepthwiseConv2  (None, 29, 29, 768)  6912       ['block4b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4b_bn (BatchNormalization  (None, 29, 29, 768)  3072       ['block4b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4b_activation (Activation  (None, 29, 29, 768)  0          ['block4b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4b_se_squeeze (GlobalAver  (None, 768)         0           ['block4b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4b_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block4b_se_squeeze[0][0]']     
                                                                                                  
 block4b_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block4b_se_reshape[0][0]']     
                                                                                                  
 block4b_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block4b_se_reduce[0][0]']      
                                                                                                  
 block4b_se_excite (Multiply)   (None, 29, 29, 768)  0           ['block4b_activation[0][0]',     
                                                                  'block4b_se_expand[0][0]']      
                                                                                                  
 block4b_project_conv (Conv2D)  (None, 29, 29, 128)  98304       ['block4b_se_excite[0][0]']      
                                                                                                  
 block4b_project_bn (BatchNorma  (None, 29, 29, 128)  512        ['block4b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4b_drop (FixedDropout)    (None, 29, 29, 128)  0           ['block4b_project_bn[0][0]']     
                                                                                                  
 block4b_add (Add)              (None, 29, 29, 128)  0           ['block4b_drop[0][0]',           
                                                                  'block4a_project_bn[0][0]']     
                                                                                                  
 block4c_expand_conv (Conv2D)   (None, 29, 29, 768)  98304       ['block4b_add[0][0]']            
                                                                                                  
 block4c_expand_bn (BatchNormal  (None, 29, 29, 768)  3072       ['block4c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4c_expand_activation (Act  (None, 29, 29, 768)  0          ['block4c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4c_dwconv (DepthwiseConv2  (None, 29, 29, 768)  6912       ['block4c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4c_bn (BatchNormalization  (None, 29, 29, 768)  3072       ['block4c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4c_activation (Activation  (None, 29, 29, 768)  0          ['block4c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4c_se_squeeze (GlobalAver  (None, 768)         0           ['block4c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4c_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block4c_se_squeeze[0][0]']     
                                                                                                  
 block4c_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block4c_se_reshape[0][0]']     
                                                                                                  
 block4c_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block4c_se_reduce[0][0]']      
                                                                                                  
 block4c_se_excite (Multiply)   (None, 29, 29, 768)  0           ['block4c_activation[0][0]',     
                                                                  'block4c_se_expand[0][0]']      
                                                                                                  
 block4c_project_conv (Conv2D)  (None, 29, 29, 128)  98304       ['block4c_se_excite[0][0]']      
                                                                                                  
 block4c_project_bn (BatchNorma  (None, 29, 29, 128)  512        ['block4c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4c_drop (FixedDropout)    (None, 29, 29, 128)  0           ['block4c_project_bn[0][0]']     
                                                                                                  
 block4c_add (Add)              (None, 29, 29, 128)  0           ['block4c_drop[0][0]',           
                                                                  'block4b_add[0][0]']            
                                                                                                  
 block4d_expand_conv (Conv2D)   (None, 29, 29, 768)  98304       ['block4c_add[0][0]']            
                                                                                                  
 block4d_expand_bn (BatchNormal  (None, 29, 29, 768)  3072       ['block4d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4d_expand_activation (Act  (None, 29, 29, 768)  0          ['block4d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4d_dwconv (DepthwiseConv2  (None, 29, 29, 768)  6912       ['block4d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4d_bn (BatchNormalization  (None, 29, 29, 768)  3072       ['block4d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4d_activation (Activation  (None, 29, 29, 768)  0          ['block4d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4d_se_squeeze (GlobalAver  (None, 768)         0           ['block4d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4d_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block4d_se_squeeze[0][0]']     
                                                                                                  
 block4d_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block4d_se_reshape[0][0]']     
                                                                                                  
 block4d_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block4d_se_reduce[0][0]']      
                                                                                                  
 block4d_se_excite (Multiply)   (None, 29, 29, 768)  0           ['block4d_activation[0][0]',     
                                                                  'block4d_se_expand[0][0]']      
                                                                                                  
 block4d_project_conv (Conv2D)  (None, 29, 29, 128)  98304       ['block4d_se_excite[0][0]']      
                                                                                                  
 block4d_project_bn (BatchNorma  (None, 29, 29, 128)  512        ['block4d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4d_drop (FixedDropout)    (None, 29, 29, 128)  0           ['block4d_project_bn[0][0]']     
                                                                                                  
 block4d_add (Add)              (None, 29, 29, 128)  0           ['block4d_drop[0][0]',           
                                                                  'block4c_add[0][0]']            
                                                                                                  
 block4e_expand_conv (Conv2D)   (None, 29, 29, 768)  98304       ['block4d_add[0][0]']            
                                                                                                  
 block4e_expand_bn (BatchNormal  (None, 29, 29, 768)  3072       ['block4e_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4e_expand_activation (Act  (None, 29, 29, 768)  0          ['block4e_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4e_dwconv (DepthwiseConv2  (None, 29, 29, 768)  6912       ['block4e_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4e_bn (BatchNormalization  (None, 29, 29, 768)  3072       ['block4e_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4e_activation (Activation  (None, 29, 29, 768)  0          ['block4e_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4e_se_squeeze (GlobalAver  (None, 768)         0           ['block4e_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4e_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block4e_se_squeeze[0][0]']     
                                                                                                  
 block4e_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block4e_se_reshape[0][0]']     
                                                                                                  
 block4e_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block4e_se_reduce[0][0]']      
                                                                                                  
 block4e_se_excite (Multiply)   (None, 29, 29, 768)  0           ['block4e_activation[0][0]',     
                                                                  'block4e_se_expand[0][0]']      
                                                                                                  
 block4e_project_conv (Conv2D)  (None, 29, 29, 128)  98304       ['block4e_se_excite[0][0]']      
                                                                                                  
 block4e_project_bn (BatchNorma  (None, 29, 29, 128)  512        ['block4e_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4e_drop (FixedDropout)    (None, 29, 29, 128)  0           ['block4e_project_bn[0][0]']     
                                                                                                  
 block4e_add (Add)              (None, 29, 29, 128)  0           ['block4e_drop[0][0]',           
                                                                  'block4d_add[0][0]']            
                                                                                                  
 block4f_expand_conv (Conv2D)   (None, 29, 29, 768)  98304       ['block4e_add[0][0]']            
                                                                                                  
 block4f_expand_bn (BatchNormal  (None, 29, 29, 768)  3072       ['block4f_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4f_expand_activation (Act  (None, 29, 29, 768)  0          ['block4f_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4f_dwconv (DepthwiseConv2  (None, 29, 29, 768)  6912       ['block4f_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4f_bn (BatchNormalization  (None, 29, 29, 768)  3072       ['block4f_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4f_activation (Activation  (None, 29, 29, 768)  0          ['block4f_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4f_se_squeeze (GlobalAver  (None, 768)         0           ['block4f_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4f_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block4f_se_squeeze[0][0]']     
                                                                                                  
 block4f_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block4f_se_reshape[0][0]']     
                                                                                                  
 block4f_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block4f_se_reduce[0][0]']      
                                                                                                  
 block4f_se_excite (Multiply)   (None, 29, 29, 768)  0           ['block4f_activation[0][0]',     
                                                                  'block4f_se_expand[0][0]']      
                                                                                                  
 block4f_project_conv (Conv2D)  (None, 29, 29, 128)  98304       ['block4f_se_excite[0][0]']      
                                                                                                  
 block4f_project_bn (BatchNorma  (None, 29, 29, 128)  512        ['block4f_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4f_drop (FixedDropout)    (None, 29, 29, 128)  0           ['block4f_project_bn[0][0]']     
                                                                                                  
 block4f_add (Add)              (None, 29, 29, 128)  0           ['block4f_drop[0][0]',           
                                                                  'block4e_add[0][0]']            
                                                                                                  
 block4g_expand_conv (Conv2D)   (None, 29, 29, 768)  98304       ['block4f_add[0][0]']            
                                                                                                  
 block4g_expand_bn (BatchNormal  (None, 29, 29, 768)  3072       ['block4g_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4g_expand_activation (Act  (None, 29, 29, 768)  0          ['block4g_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4g_dwconv (DepthwiseConv2  (None, 29, 29, 768)  6912       ['block4g_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4g_bn (BatchNormalization  (None, 29, 29, 768)  3072       ['block4g_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4g_activation (Activation  (None, 29, 29, 768)  0          ['block4g_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4g_se_squeeze (GlobalAver  (None, 768)         0           ['block4g_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4g_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block4g_se_squeeze[0][0]']     
                                                                                                  
 block4g_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block4g_se_reshape[0][0]']     
                                                                                                  
 block4g_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block4g_se_reduce[0][0]']      
                                                                                                  
 block4g_se_excite (Multiply)   (None, 29, 29, 768)  0           ['block4g_activation[0][0]',     
                                                                  'block4g_se_expand[0][0]']      
                                                                                                  
 block4g_project_conv (Conv2D)  (None, 29, 29, 128)  98304       ['block4g_se_excite[0][0]']      
                                                                                                  
 block4g_project_bn (BatchNorma  (None, 29, 29, 128)  512        ['block4g_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4g_drop (FixedDropout)    (None, 29, 29, 128)  0           ['block4g_project_bn[0][0]']     
                                                                                                  
 block4g_add (Add)              (None, 29, 29, 128)  0           ['block4g_drop[0][0]',           
                                                                  'block4f_add[0][0]']            
                                                                                                  
 block5a_expand_conv (Conv2D)   (None, 29, 29, 768)  98304       ['block4g_add[0][0]']            
                                                                                                  
 block5a_expand_bn (BatchNormal  (None, 29, 29, 768)  3072       ['block5a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5a_expand_activation (Act  (None, 29, 29, 768)  0          ['block5a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5a_dwconv (DepthwiseConv2  (None, 29, 29, 768)  19200      ['block5a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5a_bn (BatchNormalization  (None, 29, 29, 768)  3072       ['block5a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5a_activation (Activation  (None, 29, 29, 768)  0          ['block5a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5a_se_squeeze (GlobalAver  (None, 768)         0           ['block5a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5a_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block5a_se_squeeze[0][0]']     
                                                                                                  
 block5a_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block5a_se_reshape[0][0]']     
                                                                                                  
 block5a_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block5a_se_reduce[0][0]']      
                                                                                                  
 block5a_se_excite (Multiply)   (None, 29, 29, 768)  0           ['block5a_activation[0][0]',     
                                                                  'block5a_se_expand[0][0]']      
                                                                                                  
 block5a_project_conv (Conv2D)  (None, 29, 29, 176)  135168      ['block5a_se_excite[0][0]']      
                                                                                                  
 block5a_project_bn (BatchNorma  (None, 29, 29, 176)  704        ['block5a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5b_expand_conv (Conv2D)   (None, 29, 29, 1056  185856      ['block5a_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block5b_expand_bn (BatchNormal  (None, 29, 29, 1056  4224       ['block5b_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block5b_expand_activation (Act  (None, 29, 29, 1056  0          ['block5b_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block5b_dwconv (DepthwiseConv2  (None, 29, 29, 1056  26400      ['block5b_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block5b_bn (BatchNormalization  (None, 29, 29, 1056  4224       ['block5b_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block5b_activation (Activation  (None, 29, 29, 1056  0          ['block5b_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block5b_se_squeeze (GlobalAver  (None, 1056)        0           ['block5b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5b_se_reshape (Reshape)   (None, 1, 1, 1056)   0           ['block5b_se_squeeze[0][0]']     
                                                                                                  
 block5b_se_reduce (Conv2D)     (None, 1, 1, 44)     46508       ['block5b_se_reshape[0][0]']     
                                                                                                  
 block5b_se_expand (Conv2D)     (None, 1, 1, 1056)   47520       ['block5b_se_reduce[0][0]']      
                                                                                                  
 block5b_se_excite (Multiply)   (None, 29, 29, 1056  0           ['block5b_activation[0][0]',     
                                )                                 'block5b_se_expand[0][0]']      
                                                                                                  
 block5b_project_conv (Conv2D)  (None, 29, 29, 176)  185856      ['block5b_se_excite[0][0]']      
                                                                                                  
 block5b_project_bn (BatchNorma  (None, 29, 29, 176)  704        ['block5b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5b_drop (FixedDropout)    (None, 29, 29, 176)  0           ['block5b_project_bn[0][0]']     
                                                                                                  
 block5b_add (Add)              (None, 29, 29, 176)  0           ['block5b_drop[0][0]',           
                                                                  'block5a_project_bn[0][0]']     
                                                                                                  
 block5c_expand_conv (Conv2D)   (None, 29, 29, 1056  185856      ['block5b_add[0][0]']            
                                )                                                                 
                                                                                                  
 block5c_expand_bn (BatchNormal  (None, 29, 29, 1056  4224       ['block5c_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block5c_expand_activation (Act  (None, 29, 29, 1056  0          ['block5c_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block5c_dwconv (DepthwiseConv2  (None, 29, 29, 1056  26400      ['block5c_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block5c_bn (BatchNormalization  (None, 29, 29, 1056  4224       ['block5c_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block5c_activation (Activation  (None, 29, 29, 1056  0          ['block5c_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block5c_se_squeeze (GlobalAver  (None, 1056)        0           ['block5c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5c_se_reshape (Reshape)   (None, 1, 1, 1056)   0           ['block5c_se_squeeze[0][0]']     
                                                                                                  
 block5c_se_reduce (Conv2D)     (None, 1, 1, 44)     46508       ['block5c_se_reshape[0][0]']     
                                                                                                  
 block5c_se_expand (Conv2D)     (None, 1, 1, 1056)   47520       ['block5c_se_reduce[0][0]']      
                                                                                                  
 block5c_se_excite (Multiply)   (None, 29, 29, 1056  0           ['block5c_activation[0][0]',     
                                )                                 'block5c_se_expand[0][0]']      
                                                                                                  
 block5c_project_conv (Conv2D)  (None, 29, 29, 176)  185856      ['block5c_se_excite[0][0]']      
                                                                                                  
 block5c_project_bn (BatchNorma  (None, 29, 29, 176)  704        ['block5c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5c_drop (FixedDropout)    (None, 29, 29, 176)  0           ['block5c_project_bn[0][0]']     
                                                                                                  
 block5c_add (Add)              (None, 29, 29, 176)  0           ['block5c_drop[0][0]',           
                                                                  'block5b_add[0][0]']            
                                                                                                  
 block5d_expand_conv (Conv2D)   (None, 29, 29, 1056  185856      ['block5c_add[0][0]']            
                                )                                                                 
                                                                                                  
 block5d_expand_bn (BatchNormal  (None, 29, 29, 1056  4224       ['block5d_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block5d_expand_activation (Act  (None, 29, 29, 1056  0          ['block5d_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block5d_dwconv (DepthwiseConv2  (None, 29, 29, 1056  26400      ['block5d_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block5d_bn (BatchNormalization  (None, 29, 29, 1056  4224       ['block5d_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block5d_activation (Activation  (None, 29, 29, 1056  0          ['block5d_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block5d_se_squeeze (GlobalAver  (None, 1056)        0           ['block5d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5d_se_reshape (Reshape)   (None, 1, 1, 1056)   0           ['block5d_se_squeeze[0][0]']     
                                                                                                  
 block5d_se_reduce (Conv2D)     (None, 1, 1, 44)     46508       ['block5d_se_reshape[0][0]']     
                                                                                                  
 block5d_se_expand (Conv2D)     (None, 1, 1, 1056)   47520       ['block5d_se_reduce[0][0]']      
                                                                                                  
 block5d_se_excite (Multiply)   (None, 29, 29, 1056  0           ['block5d_activation[0][0]',     
                                )                                 'block5d_se_expand[0][0]']      
                                                                                                  
 block5d_project_conv (Conv2D)  (None, 29, 29, 176)  185856      ['block5d_se_excite[0][0]']      
                                                                                                  
 block5d_project_bn (BatchNorma  (None, 29, 29, 176)  704        ['block5d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5d_drop (FixedDropout)    (None, 29, 29, 176)  0           ['block5d_project_bn[0][0]']     
                                                                                                  
 block5d_add (Add)              (None, 29, 29, 176)  0           ['block5d_drop[0][0]',           
                                                                  'block5c_add[0][0]']            
                                                                                                  
 block5e_expand_conv (Conv2D)   (None, 29, 29, 1056  185856      ['block5d_add[0][0]']            
                                )                                                                 
                                                                                                  
 block5e_expand_bn (BatchNormal  (None, 29, 29, 1056  4224       ['block5e_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block5e_expand_activation (Act  (None, 29, 29, 1056  0          ['block5e_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block5e_dwconv (DepthwiseConv2  (None, 29, 29, 1056  26400      ['block5e_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block5e_bn (BatchNormalization  (None, 29, 29, 1056  4224       ['block5e_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block5e_activation (Activation  (None, 29, 29, 1056  0          ['block5e_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block5e_se_squeeze (GlobalAver  (None, 1056)        0           ['block5e_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5e_se_reshape (Reshape)   (None, 1, 1, 1056)   0           ['block5e_se_squeeze[0][0]']     
                                                                                                  
 block5e_se_reduce (Conv2D)     (None, 1, 1, 44)     46508       ['block5e_se_reshape[0][0]']     
                                                                                                  
 block5e_se_expand (Conv2D)     (None, 1, 1, 1056)   47520       ['block5e_se_reduce[0][0]']      
                                                                                                  
 block5e_se_excite (Multiply)   (None, 29, 29, 1056  0           ['block5e_activation[0][0]',     
                                )                                 'block5e_se_expand[0][0]']      
                                                                                                  
 block5e_project_conv (Conv2D)  (None, 29, 29, 176)  185856      ['block5e_se_excite[0][0]']      
                                                                                                  
 block5e_project_bn (BatchNorma  (None, 29, 29, 176)  704        ['block5e_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5e_drop (FixedDropout)    (None, 29, 29, 176)  0           ['block5e_project_bn[0][0]']     
                                                                                                  
 block5e_add (Add)              (None, 29, 29, 176)  0           ['block5e_drop[0][0]',           
                                                                  'block5d_add[0][0]']            
                                                                                                  
 block5f_expand_conv (Conv2D)   (None, 29, 29, 1056  185856      ['block5e_add[0][0]']            
                                )                                                                 
                                                                                                  
 block5f_expand_bn (BatchNormal  (None, 29, 29, 1056  4224       ['block5f_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block5f_expand_activation (Act  (None, 29, 29, 1056  0          ['block5f_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block5f_dwconv (DepthwiseConv2  (None, 29, 29, 1056  26400      ['block5f_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block5f_bn (BatchNormalization  (None, 29, 29, 1056  4224       ['block5f_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block5f_activation (Activation  (None, 29, 29, 1056  0          ['block5f_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block5f_se_squeeze (GlobalAver  (None, 1056)        0           ['block5f_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5f_se_reshape (Reshape)   (None, 1, 1, 1056)   0           ['block5f_se_squeeze[0][0]']     
                                                                                                  
 block5f_se_reduce (Conv2D)     (None, 1, 1, 44)     46508       ['block5f_se_reshape[0][0]']     
                                                                                                  
 block5f_se_expand (Conv2D)     (None, 1, 1, 1056)   47520       ['block5f_se_reduce[0][0]']      
                                                                                                  
 block5f_se_excite (Multiply)   (None, 29, 29, 1056  0           ['block5f_activation[0][0]',     
                                )                                 'block5f_se_expand[0][0]']      
                                                                                                  
 block5f_project_conv (Conv2D)  (None, 29, 29, 176)  185856      ['block5f_se_excite[0][0]']      
                                                                                                  
 block5f_project_bn (BatchNorma  (None, 29, 29, 176)  704        ['block5f_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5f_drop (FixedDropout)    (None, 29, 29, 176)  0           ['block5f_project_bn[0][0]']     
                                                                                                  
 block5f_add (Add)              (None, 29, 29, 176)  0           ['block5f_drop[0][0]',           
                                                                  'block5e_add[0][0]']            
                                                                                                  
 block5g_expand_conv (Conv2D)   (None, 29, 29, 1056  185856      ['block5f_add[0][0]']            
                                )                                                                 
                                                                                                  
 block5g_expand_bn (BatchNormal  (None, 29, 29, 1056  4224       ['block5g_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block5g_expand_activation (Act  (None, 29, 29, 1056  0          ['block5g_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block5g_dwconv (DepthwiseConv2  (None, 29, 29, 1056  26400      ['block5g_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block5g_bn (BatchNormalization  (None, 29, 29, 1056  4224       ['block5g_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block5g_activation (Activation  (None, 29, 29, 1056  0          ['block5g_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block5g_se_squeeze (GlobalAver  (None, 1056)        0           ['block5g_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5g_se_reshape (Reshape)   (None, 1, 1, 1056)   0           ['block5g_se_squeeze[0][0]']     
                                                                                                  
 block5g_se_reduce (Conv2D)     (None, 1, 1, 44)     46508       ['block5g_se_reshape[0][0]']     
                                                                                                  
 block5g_se_expand (Conv2D)     (None, 1, 1, 1056)   47520       ['block5g_se_reduce[0][0]']      
                                                                                                  
 block5g_se_excite (Multiply)   (None, 29, 29, 1056  0           ['block5g_activation[0][0]',     
                                )                                 'block5g_se_expand[0][0]']      
                                                                                                  
 block5g_project_conv (Conv2D)  (None, 29, 29, 176)  185856      ['block5g_se_excite[0][0]']      
                                                                                                  
 block5g_project_bn (BatchNorma  (None, 29, 29, 176)  704        ['block5g_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5g_drop (FixedDropout)    (None, 29, 29, 176)  0           ['block5g_project_bn[0][0]']     
                                                                                                  
 block5g_add (Add)              (None, 29, 29, 176)  0           ['block5g_drop[0][0]',           
                                                                  'block5f_add[0][0]']            
                                                                                                  
 block6a_expand_conv (Conv2D)   (None, 29, 29, 1056  185856      ['block5g_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6a_expand_bn (BatchNormal  (None, 29, 29, 1056  4224       ['block6a_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6a_expand_activation (Act  (None, 29, 29, 1056  0          ['block6a_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6a_dwconv (DepthwiseConv2  (None, 15, 15, 1056  26400      ['block6a_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6a_bn (BatchNormalization  (None, 15, 15, 1056  4224       ['block6a_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6a_activation (Activation  (None, 15, 15, 1056  0          ['block6a_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6a_se_squeeze (GlobalAver  (None, 1056)        0           ['block6a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6a_se_reshape (Reshape)   (None, 1, 1, 1056)   0           ['block6a_se_squeeze[0][0]']     
                                                                                                  
 block6a_se_reduce (Conv2D)     (None, 1, 1, 44)     46508       ['block6a_se_reshape[0][0]']     
                                                                                                  
 block6a_se_expand (Conv2D)     (None, 1, 1, 1056)   47520       ['block6a_se_reduce[0][0]']      
                                                                                                  
 block6a_se_excite (Multiply)   (None, 15, 15, 1056  0           ['block6a_activation[0][0]',     
                                )                                 'block6a_se_expand[0][0]']      
                                                                                                  
 block6a_project_conv (Conv2D)  (None, 15, 15, 304)  321024      ['block6a_se_excite[0][0]']      
                                                                                                  
 block6a_project_bn (BatchNorma  (None, 15, 15, 304)  1216       ['block6a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6b_expand_conv (Conv2D)   (None, 15, 15, 1824  554496      ['block6a_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block6b_expand_bn (BatchNormal  (None, 15, 15, 1824  7296       ['block6b_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6b_expand_activation (Act  (None, 15, 15, 1824  0          ['block6b_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6b_dwconv (DepthwiseConv2  (None, 15, 15, 1824  45600      ['block6b_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6b_bn (BatchNormalization  (None, 15, 15, 1824  7296       ['block6b_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6b_activation (Activation  (None, 15, 15, 1824  0          ['block6b_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6b_se_squeeze (GlobalAver  (None, 1824)        0           ['block6b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6b_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6b_se_squeeze[0][0]']     
                                                                                                  
 block6b_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6b_se_reshape[0][0]']     
                                                                                                  
 block6b_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6b_se_reduce[0][0]']      
                                                                                                  
 block6b_se_excite (Multiply)   (None, 15, 15, 1824  0           ['block6b_activation[0][0]',     
                                )                                 'block6b_se_expand[0][0]']      
                                                                                                  
 block6b_project_conv (Conv2D)  (None, 15, 15, 304)  554496      ['block6b_se_excite[0][0]']      
                                                                                                  
 block6b_project_bn (BatchNorma  (None, 15, 15, 304)  1216       ['block6b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6b_drop (FixedDropout)    (None, 15, 15, 304)  0           ['block6b_project_bn[0][0]']     
                                                                                                  
 block6b_add (Add)              (None, 15, 15, 304)  0           ['block6b_drop[0][0]',           
                                                                  'block6a_project_bn[0][0]']     
                                                                                                  
 block6c_expand_conv (Conv2D)   (None, 15, 15, 1824  554496      ['block6b_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6c_expand_bn (BatchNormal  (None, 15, 15, 1824  7296       ['block6c_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6c_expand_activation (Act  (None, 15, 15, 1824  0          ['block6c_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6c_dwconv (DepthwiseConv2  (None, 15, 15, 1824  45600      ['block6c_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6c_bn (BatchNormalization  (None, 15, 15, 1824  7296       ['block6c_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6c_activation (Activation  (None, 15, 15, 1824  0          ['block6c_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6c_se_squeeze (GlobalAver  (None, 1824)        0           ['block6c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6c_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6c_se_squeeze[0][0]']     
                                                                                                  
 block6c_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6c_se_reshape[0][0]']     
                                                                                                  
 block6c_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6c_se_reduce[0][0]']      
                                                                                                  
 block6c_se_excite (Multiply)   (None, 15, 15, 1824  0           ['block6c_activation[0][0]',     
                                )                                 'block6c_se_expand[0][0]']      
                                                                                                  
 block6c_project_conv (Conv2D)  (None, 15, 15, 304)  554496      ['block6c_se_excite[0][0]']      
                                                                                                  
 block6c_project_bn (BatchNorma  (None, 15, 15, 304)  1216       ['block6c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6c_drop (FixedDropout)    (None, 15, 15, 304)  0           ['block6c_project_bn[0][0]']     
                                                                                                  
 block6c_add (Add)              (None, 15, 15, 304)  0           ['block6c_drop[0][0]',           
                                                                  'block6b_add[0][0]']            
                                                                                                  
 block6d_expand_conv (Conv2D)   (None, 15, 15, 1824  554496      ['block6c_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6d_expand_bn (BatchNormal  (None, 15, 15, 1824  7296       ['block6d_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6d_expand_activation (Act  (None, 15, 15, 1824  0          ['block6d_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6d_dwconv (DepthwiseConv2  (None, 15, 15, 1824  45600      ['block6d_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6d_bn (BatchNormalization  (None, 15, 15, 1824  7296       ['block6d_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6d_activation (Activation  (None, 15, 15, 1824  0          ['block6d_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6d_se_squeeze (GlobalAver  (None, 1824)        0           ['block6d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6d_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6d_se_squeeze[0][0]']     
                                                                                                  
 block6d_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6d_se_reshape[0][0]']     
                                                                                                  
 block6d_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6d_se_reduce[0][0]']      
                                                                                                  
 block6d_se_excite (Multiply)   (None, 15, 15, 1824  0           ['block6d_activation[0][0]',     
                                )                                 'block6d_se_expand[0][0]']      
                                                                                                  
 block6d_project_conv (Conv2D)  (None, 15, 15, 304)  554496      ['block6d_se_excite[0][0]']      
                                                                                                  
 block6d_project_bn (BatchNorma  (None, 15, 15, 304)  1216       ['block6d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6d_drop (FixedDropout)    (None, 15, 15, 304)  0           ['block6d_project_bn[0][0]']     
                                                                                                  
 block6d_add (Add)              (None, 15, 15, 304)  0           ['block6d_drop[0][0]',           
                                                                  'block6c_add[0][0]']            
                                                                                                  
 block6e_expand_conv (Conv2D)   (None, 15, 15, 1824  554496      ['block6d_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6e_expand_bn (BatchNormal  (None, 15, 15, 1824  7296       ['block6e_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6e_expand_activation (Act  (None, 15, 15, 1824  0          ['block6e_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6e_dwconv (DepthwiseConv2  (None, 15, 15, 1824  45600      ['block6e_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6e_bn (BatchNormalization  (None, 15, 15, 1824  7296       ['block6e_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6e_activation (Activation  (None, 15, 15, 1824  0          ['block6e_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6e_se_squeeze (GlobalAver  (None, 1824)        0           ['block6e_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6e_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6e_se_squeeze[0][0]']     
                                                                                                  
 block6e_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6e_se_reshape[0][0]']     
                                                                                                  
 block6e_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6e_se_reduce[0][0]']      
                                                                                                  
 block6e_se_excite (Multiply)   (None, 15, 15, 1824  0           ['block6e_activation[0][0]',     
                                )                                 'block6e_se_expand[0][0]']      
                                                                                                  
 block6e_project_conv (Conv2D)  (None, 15, 15, 304)  554496      ['block6e_se_excite[0][0]']      
                                                                                                  
 block6e_project_bn (BatchNorma  (None, 15, 15, 304)  1216       ['block6e_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6e_drop (FixedDropout)    (None, 15, 15, 304)  0           ['block6e_project_bn[0][0]']     
                                                                                                  
 block6e_add (Add)              (None, 15, 15, 304)  0           ['block6e_drop[0][0]',           
                                                                  'block6d_add[0][0]']            
                                                                                                  
 block6f_expand_conv (Conv2D)   (None, 15, 15, 1824  554496      ['block6e_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6f_expand_bn (BatchNormal  (None, 15, 15, 1824  7296       ['block6f_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6f_expand_activation (Act  (None, 15, 15, 1824  0          ['block6f_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6f_dwconv (DepthwiseConv2  (None, 15, 15, 1824  45600      ['block6f_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6f_bn (BatchNormalization  (None, 15, 15, 1824  7296       ['block6f_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6f_activation (Activation  (None, 15, 15, 1824  0          ['block6f_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6f_se_squeeze (GlobalAver  (None, 1824)        0           ['block6f_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6f_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6f_se_squeeze[0][0]']     
                                                                                                  
 block6f_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6f_se_reshape[0][0]']     
                                                                                                  
 block6f_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6f_se_reduce[0][0]']      
                                                                                                  
 block6f_se_excite (Multiply)   (None, 15, 15, 1824  0           ['block6f_activation[0][0]',     
                                )                                 'block6f_se_expand[0][0]']      
                                                                                                  
 block6f_project_conv (Conv2D)  (None, 15, 15, 304)  554496      ['block6f_se_excite[0][0]']      
                                                                                                  
 block6f_project_bn (BatchNorma  (None, 15, 15, 304)  1216       ['block6f_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6f_drop (FixedDropout)    (None, 15, 15, 304)  0           ['block6f_project_bn[0][0]']     
                                                                                                  
 block6f_add (Add)              (None, 15, 15, 304)  0           ['block6f_drop[0][0]',           
                                                                  'block6e_add[0][0]']            
                                                                                                  
 block6g_expand_conv (Conv2D)   (None, 15, 15, 1824  554496      ['block6f_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6g_expand_bn (BatchNormal  (None, 15, 15, 1824  7296       ['block6g_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6g_expand_activation (Act  (None, 15, 15, 1824  0          ['block6g_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6g_dwconv (DepthwiseConv2  (None, 15, 15, 1824  45600      ['block6g_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6g_bn (BatchNormalization  (None, 15, 15, 1824  7296       ['block6g_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6g_activation (Activation  (None, 15, 15, 1824  0          ['block6g_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6g_se_squeeze (GlobalAver  (None, 1824)        0           ['block6g_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6g_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6g_se_squeeze[0][0]']     
                                                                                                  
 block6g_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6g_se_reshape[0][0]']     
                                                                                                  
 block6g_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6g_se_reduce[0][0]']      
                                                                                                  
 block6g_se_excite (Multiply)   (None, 15, 15, 1824  0           ['block6g_activation[0][0]',     
                                )                                 'block6g_se_expand[0][0]']      
                                                                                                  
 block6g_project_conv (Conv2D)  (None, 15, 15, 304)  554496      ['block6g_se_excite[0][0]']      
                                                                                                  
 block6g_project_bn (BatchNorma  (None, 15, 15, 304)  1216       ['block6g_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6g_drop (FixedDropout)    (None, 15, 15, 304)  0           ['block6g_project_bn[0][0]']     
                                                                                                  
 block6g_add (Add)              (None, 15, 15, 304)  0           ['block6g_drop[0][0]',           
                                                                  'block6f_add[0][0]']            
                                                                                                  
 block6h_expand_conv (Conv2D)   (None, 15, 15, 1824  554496      ['block6g_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6h_expand_bn (BatchNormal  (None, 15, 15, 1824  7296       ['block6h_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6h_expand_activation (Act  (None, 15, 15, 1824  0          ['block6h_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6h_dwconv (DepthwiseConv2  (None, 15, 15, 1824  45600      ['block6h_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6h_bn (BatchNormalization  (None, 15, 15, 1824  7296       ['block6h_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6h_activation (Activation  (None, 15, 15, 1824  0          ['block6h_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6h_se_squeeze (GlobalAver  (None, 1824)        0           ['block6h_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6h_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6h_se_squeeze[0][0]']     
                                                                                                  
 block6h_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6h_se_reshape[0][0]']     
                                                                                                  
 block6h_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6h_se_reduce[0][0]']      
                                                                                                  
 block6h_se_excite (Multiply)   (None, 15, 15, 1824  0           ['block6h_activation[0][0]',     
                                )                                 'block6h_se_expand[0][0]']      
                                                                                                  
 block6h_project_conv (Conv2D)  (None, 15, 15, 304)  554496      ['block6h_se_excite[0][0]']      
                                                                                                  
 block6h_project_bn (BatchNorma  (None, 15, 15, 304)  1216       ['block6h_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6h_drop (FixedDropout)    (None, 15, 15, 304)  0           ['block6h_project_bn[0][0]']     
                                                                                                  
 block6h_add (Add)              (None, 15, 15, 304)  0           ['block6h_drop[0][0]',           
                                                                  'block6g_add[0][0]']            
                                                                                                  
 block6i_expand_conv (Conv2D)   (None, 15, 15, 1824  554496      ['block6h_add[0][0]']            
                                )                                                                 
                                                                                                  
 block6i_expand_bn (BatchNormal  (None, 15, 15, 1824  7296       ['block6i_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block6i_expand_activation (Act  (None, 15, 15, 1824  0          ['block6i_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block6i_dwconv (DepthwiseConv2  (None, 15, 15, 1824  45600      ['block6i_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block6i_bn (BatchNormalization  (None, 15, 15, 1824  7296       ['block6i_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block6i_activation (Activation  (None, 15, 15, 1824  0          ['block6i_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block6i_se_squeeze (GlobalAver  (None, 1824)        0           ['block6i_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6i_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6i_se_squeeze[0][0]']     
                                                                                                  
 block6i_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6i_se_reshape[0][0]']     
                                                                                                  
 block6i_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6i_se_reduce[0][0]']      
                                                                                                  
 block6i_se_excite (Multiply)   (None, 15, 15, 1824  0           ['block6i_activation[0][0]',     
                                )                                 'block6i_se_expand[0][0]']      
                                                                                                  
 block6i_project_conv (Conv2D)  (None, 15, 15, 304)  554496      ['block6i_se_excite[0][0]']      
                                                                                                  
 block6i_project_bn (BatchNorma  (None, 15, 15, 304)  1216       ['block6i_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6i_drop (FixedDropout)    (None, 15, 15, 304)  0           ['block6i_project_bn[0][0]']     
                                                                                                  
 block6i_add (Add)              (None, 15, 15, 304)  0           ['block6i_drop[0][0]',           
                                                                  'block6h_add[0][0]']            
                                                                                                  
 block7a_expand_conv (Conv2D)   (None, 15, 15, 1824  554496      ['block6i_add[0][0]']            
                                )                                                                 
                                                                                                  
 block7a_expand_bn (BatchNormal  (None, 15, 15, 1824  7296       ['block7a_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block7a_expand_activation (Act  (None, 15, 15, 1824  0          ['block7a_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block7a_dwconv (DepthwiseConv2  (None, 15, 15, 1824  16416      ['block7a_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block7a_bn (BatchNormalization  (None, 15, 15, 1824  7296       ['block7a_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block7a_activation (Activation  (None, 15, 15, 1824  0          ['block7a_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block7a_se_squeeze (GlobalAver  (None, 1824)        0           ['block7a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block7a_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block7a_se_squeeze[0][0]']     
                                                                                                  
 block7a_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block7a_se_reshape[0][0]']     
                                                                                                  
 block7a_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block7a_se_reduce[0][0]']      
                                                                                                  
 block7a_se_excite (Multiply)   (None, 15, 15, 1824  0           ['block7a_activation[0][0]',     
                                )                                 'block7a_se_expand[0][0]']      
                                                                                                  
 block7a_project_conv (Conv2D)  (None, 15, 15, 512)  933888      ['block7a_se_excite[0][0]']      
                                                                                                  
 block7a_project_bn (BatchNorma  (None, 15, 15, 512)  2048       ['block7a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block7b_expand_conv (Conv2D)   (None, 15, 15, 3072  1572864     ['block7a_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block7b_expand_bn (BatchNormal  (None, 15, 15, 3072  12288      ['block7b_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block7b_expand_activation (Act  (None, 15, 15, 3072  0          ['block7b_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block7b_dwconv (DepthwiseConv2  (None, 15, 15, 3072  27648      ['block7b_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block7b_bn (BatchNormalization  (None, 15, 15, 3072  12288      ['block7b_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block7b_activation (Activation  (None, 15, 15, 3072  0          ['block7b_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block7b_se_squeeze (GlobalAver  (None, 3072)        0           ['block7b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block7b_se_reshape (Reshape)   (None, 1, 1, 3072)   0           ['block7b_se_squeeze[0][0]']     
                                                                                                  
 block7b_se_reduce (Conv2D)     (None, 1, 1, 128)    393344      ['block7b_se_reshape[0][0]']     
                                                                                                  
 block7b_se_expand (Conv2D)     (None, 1, 1, 3072)   396288      ['block7b_se_reduce[0][0]']      
                                                                                                  
 block7b_se_excite (Multiply)   (None, 15, 15, 3072  0           ['block7b_activation[0][0]',     
                                )                                 'block7b_se_expand[0][0]']      
                                                                                                  
 block7b_project_conv (Conv2D)  (None, 15, 15, 512)  1572864     ['block7b_se_excite[0][0]']      
                                                                                                  
 block7b_project_bn (BatchNorma  (None, 15, 15, 512)  2048       ['block7b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block7b_drop (FixedDropout)    (None, 15, 15, 512)  0           ['block7b_project_bn[0][0]']     
                                                                                                  
 block7b_add (Add)              (None, 15, 15, 512)  0           ['block7b_drop[0][0]',           
                                                                  'block7a_project_bn[0][0]']     
                                                                                                  
 block7c_expand_conv (Conv2D)   (None, 15, 15, 3072  1572864     ['block7b_add[0][0]']            
                                )                                                                 
                                                                                                  
 block7c_expand_bn (BatchNormal  (None, 15, 15, 3072  12288      ['block7c_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block7c_expand_activation (Act  (None, 15, 15, 3072  0          ['block7c_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block7c_dwconv (DepthwiseConv2  (None, 15, 15, 3072  27648      ['block7c_expand_activation[0][0]
 D)                             )                                ']                               
                                                                                                  
 block7c_bn (BatchNormalization  (None, 15, 15, 3072  12288      ['block7c_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block7c_activation (Activation  (None, 15, 15, 3072  0          ['block7c_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block7c_se_squeeze (GlobalAver  (None, 3072)        0           ['block7c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block7c_se_reshape (Reshape)   (None, 1, 1, 3072)   0           ['block7c_se_squeeze[0][0]']     
                                                                                                  
 block7c_se_reduce (Conv2D)     (None, 1, 1, 128)    393344      ['block7c_se_reshape[0][0]']     
                                                                                                  
 block7c_se_expand (Conv2D)     (None, 1, 1, 3072)   396288      ['block7c_se_reduce[0][0]']      
                                                                                                  
 block7c_se_excite (Multiply)   (None, 15, 15, 3072  0           ['block7c_activation[0][0]',     
                                )                                 'block7c_se_expand[0][0]']      
                                                                                                  
 block7c_project_conv (Conv2D)  (None, 15, 15, 512)  1572864     ['block7c_se_excite[0][0]']      
                                                                                                  
 block7c_project_bn (BatchNorma  (None, 15, 15, 512)  2048       ['block7c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block7c_drop (FixedDropout)    (None, 15, 15, 512)  0           ['block7c_project_bn[0][0]']     
                                                                                                  
 block7c_add (Add)              (None, 15, 15, 512)  0           ['block7c_drop[0][0]',           
                                                                  'block7b_add[0][0]']            
                                                                                                  
 top_conv (Conv2D)              (None, 15, 15, 2048  1048576     ['block7c_add[0][0]']            
                                )                                                                 
                                                                                                  
 top_bn (BatchNormalization)    (None, 15, 15, 2048  8192        ['top_conv[0][0]']               
                                )                                                                 
                                                                                                  
 top_activation (Activation)    (None, 15, 15, 2048  0           ['top_bn[0][0]']                 
                                )                                                                 
                                                                                                  
 flatten_5 (Flatten)            (None, 460800)       0           ['top_activation[0][0]']         
                                                                                                  
 dense_10 (Dense)               (None, 1024)         471860224   ['flatten_5[0][0]']              
                                                                                                  
 dropout_5 (Dropout)            (None, 1024)         0           ['dense_10[0][0]']               
                                                                                                  
 dense_11 (Dense)               (None, 1)            1025        ['dropout_5[0][0]']              
                                                                                                  
==================================================================================================
Total params: 500,374,769
Trainable params: 471,861,249
Non-trainable params: 28,513,520
__________________________________________________________________________________________________

#get total parameters
model_params = model_final.count_params()

# Specify the optimizer, loss function and evaluation metrics. 
model_final.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001), metrics=['accuracy'])

t1 = time.time()
#train the model
eff_history = model_final.fit_generator(train_generator, validation_data = validation_generator, steps_per_epoch = 100, epochs = 10)
fit_time = time.time() - t1

<ipython-input-123-b7f31b017b18>:3: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  eff_history = model_final.fit_generator(train_generator, validation_data = validation_generator, steps_per_epoch = 100, epochs = 10)

Epoch 1/10
100/100 [==============================] - 138s 1s/step - loss: 1.0984 - accuracy: 0.9335 - val_loss: 0.1210 - val_accuracy: 0.9950
Epoch 2/10
100/100 [==============================] - 117s 1s/step - loss: 0.7081 - accuracy: 0.9645 - val_loss: 0.1103 - val_accuracy: 0.9937
Epoch 3/10
100/100 [==============================] - 117s 1s/step - loss: 0.8698 - accuracy: 0.9580 - val_loss: 0.1068 - val_accuracy: 0.9962
Epoch 4/10
100/100 [==============================] - 116s 1s/step - loss: 0.4198 - accuracy: 0.9775 - val_loss: 0.0582 - val_accuracy: 0.9950
Epoch 5/10
100/100 [==============================] - 116s 1s/step - loss: 1.0802 - accuracy: 0.9625 - val_loss: 0.0098 - val_accuracy: 0.9987
Epoch 6/10
100/100 [==============================] - 115s 1s/step - loss: 0.6919 - accuracy: 0.9743 - val_loss: 0.1645 - val_accuracy: 0.9937
Epoch 7/10
100/100 [==============================] - 117s 1s/step - loss: 0.6089 - accuracy: 0.9750 - val_loss: 0.0482 - val_accuracy: 0.9975
Epoch 8/10
100/100 [==============================] - 116s 1s/step - loss: 0.6994 - accuracy: 0.9798 - val_loss: 0.1010 - val_accuracy: 0.9962
Epoch 9/10
100/100 [==============================] - 116s 1s/step - loss: 0.8074 - accuracy: 0.9713 - val_loss: 0.0526 - val_accuracy: 0.9975
Epoch 10/10
100/100 [==============================] - 116s 1s/step - loss: 0.8600 - accuracy: 0.9680 - val_loss: 0.0529 - val_accuracy: 0.9950

# time it took to fit the model
print(fit_time) 

1203.5126857757568

#Plot training and validation accuracy and loss for each epoch
acc = eff_history.history['accuracy']
val_acc = eff_history.history['val_accuracy']
loss = eff_history.history['loss']
val_loss = eff_history.history['val_loss']

epochs = range(1,len(acc) + 1)

plt.plot(epochs,acc,label = 'Training Accuracy')
plt.plot(epochs,val_acc,label = 'Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()
plt.figure()

plt.plot(epochs,loss,label = 'Training loss')
plt.plot(epochs,val_loss,label = 'Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()

plt.show()

# Test dataset 
test_datagen = ImageDataGenerator(rescale=1./255)

test_generator = test_datagen.flow_from_directory(
        test_dir,
        target_size=(456, 456),
        shuffle = False,
        class_mode='binary',
        batch_size=1)

Found 2023 images belonging to 2 classes.

#Get test length
filenames = test_generator.filenames
nb_samples = len(filenames)

#Predict on test set
predict = model_final.predict_generator(test_generator,steps = nb_samples)

<ipython-input-128-7710eff794cf>:2: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.
  predict = model_final.predict_generator(test_generator,steps = nb_samples)

#Get list of prediction results
pred_list = []
for i in predict:
    if i > 0.5:
        result = 1 #dog
        pred_list.append(result)
    else:
      result = 0 #cat
      pred_list.append(result)

#Create dataframe of image ID, image true label, image predicted label
import pandas as pd
image_ids = [name.split('/')[-1] for name in test_generator.filenames]
image_label = [name.split('/')[0] for name in test_generator.filenames]
data = {'id': image_ids, 'label':image_label, 'prediction':pred_list} 
data_df = pd.DataFrame(data)
data_df.label.replace(('cats', 'dogs'), (0, 1), inplace=True) # change cat and dog label to 0 or 1

#Get test accuracy score
from sklearn.metrics import accuracy_score, confusion_matrix
test_accuracy = accuracy_score(data_df['label'], data_df['prediction'])
print('Test Accuracy: ', round((test_accuracy * 100), 2), "%")

Test Accuracy:  99.46 %

from sklearn.metrics import classification_report
#Classification Report
print(classification_report(data_df['label'], data_df['prediction']))

              precision    recall  f1-score   support

           0       1.00      0.99      0.99      1011
           1       0.99      1.00      0.99      1012

    accuracy                           0.99      2023
   macro avg       0.99      0.99      0.99      2023
weighted avg       0.99      0.99      0.99      2023

#Create confusion matrix
import seaborn as sns
label = [0, 1] #0 = cat and 1 = dog
cm = confusion_matrix(data_df['label'], data_df['prediction'], labels = label)
#Plot
ax= plt.subplot()
sns.heatmap(cm, annot=True, fmt='g', ax=ax);
# labels, title and ticks
ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); 
ax.set_title('Confusion Matrix'); 
ax.xaxis.set_ticklabels(["Cat", "Dog"]); ax.yaxis.set_ticklabels(["Cat", "Dog"])

[Text(0, 0.5, 'Cat'), Text(0, 1.5, 'Dog')]

ExperimentLog.loc[len(ExperimentLog)] = [
    "EfficientNet B5",
    456,
    "RMSprop", 
    10, 
    max(acc), 
    max(val_acc),
    test_accuracy,
    fit_time,
    model_params
]

ExperimentLog 

	Base Model	Input Resolution	Optimizer	Epochs	Training Accuracy	Validation Accuracy	Test Accuracy	Fit Time	Total Parameters
0	EfficientNet B0	224	RMSprop	10	0.951500	0.98750	0.971330	2195.619411	68276893
1	EfficientNet B0 with decay	224	RMSprop	10	0.956171	0.98750	0.974790	381.655278	68276893
2	EfficientNet B1	240	RMSprop	10	0.959698	0.99125	0.985171	349.486195	90463361
3	EfficientNet B2	260	RMSprop	10	0.965239	0.99500	0.983193	380.205944	124555763
4	EfficientNet B3	300	RMSprop	10	0.970277	0.99750	0.988631	516.328792	168071977
5	EfficientNet B4	380	RMSprop	10	0.973300	0.99875	0.987148	787.670477	281917017
6	EfficientNet B5	456	RMSprop	10	0.979849	0.99875	0.994563	1203.512686	500374769

4.9 Results and Discussion

4.9.1 CNN

Reminder (CNN Adam Optimizer epoch 25 results):

Important CNN vs MLP: Our comparison is made using Adam Optimizer and highest scores from both Phases4 vs Phase3.

In general, our Keras CNN Classification Model performs well. Throughout the entire CNN section, we could see how the CNN was performing when switching optimizers and how big improvement it got regarding Phase 3. The Adam optimizer just shows it’s the to go optimizer regarding this whole project. Adam returns 76.2% for train accuracy and a 76% for validation accuracy. Meanwhile, RMSprop returns 71% for train accuracy and 71.3% for validation accuracy. Also, Adam optimizer returns a solid test accuracy of 76% vs RMSprop test accuracy of 70%.

Finally, if we compare Phase 4 vs Phase 3, the improvement is outstanding. Just by looking at the training scores, test scores, classification report, and confusion matrix we can see that this new CNN model is overall better than Phase 3 MLP model (Please read section 4.5.5 Classification Report and Confusion Matrix). Both models had an easier time classifying dog images rather than cat images, however results were very different. The CNN model classifies and predicts for the cat class, this is something the MLP model was struggling with since the beginning (it only classified 7% of cat images). Scores, CNN Adam returns a 76% for test accuracy, highest train accuracy of 76.2%, and highest validation accuracy of 69%. Phase 3 MLP returned a 55.28% for test accuracy, a 55.14% for train accuracy, and almost 57% for validation accuracy. After giving a detailed explanation throughout the CNN section, we can positively say that our new model learned much more than the MLP model, due to this we ended up having great results.

CNN Adam (Highest)

PyTorch MLP 20 epochs (Highest)

4.9.2 FCN

Several experiments outside of this notebook were done to establish optimal dropout rate, number of hidden layers, and focusing on a larger number of epochs. Adam optimizer was assumed to perform the best but stochastic gradient descent (SGD) was used for comparison. The two models with Adam optimizers performed better based on training and testing accuracies and lower overall loss functions.

Their testing accuracies and loss functions were exactly the same 0.592 and 0.676. The model with batch size of 25 had slightly higher training accuracy 0.622 (compared to the validation and testing accuracies) which suggests a slight overfitting but its validation and testing accuracies are the same as the other Adam model with larger batch size. Also when taken into account precision from the confusion matrix was slightly higher at recognizing cats and dogs, the last model with Adam optimizer and batch size of 50 performed the best from the three available ones.

4.9.3 AlexNet

After implementing the Alexnet model from phase 3, it was noted the input shape from our original reshape model of (128,128,3) did not match the architecture for kernel output. Upon this discovery, I wanted to reshape the data to match the model for (227,227,3) to improve accuracy scores. After adjustment, accuracy scores already showed a huge improvement( as seen below) in this phase from train accuracy of 53.8% and loss 0.6897, and validation accuracy of 53.8% and loss 0.6857 after 20 epochs in phase 3, to a train of 81.57% accuracy and 0.3866 loss, and validation of 84.73% accuracy and 0.3413 loss after 30 epochs in phase 4. Even though more epochs were done in phase 4, looking at the 20 epoch mark in this phase there is still notable improvement from the prior model. It is also believed if there were more time to run a larger epoch size, like a 100, there would be even more room for improvement of greater accuracy scores and lower losses.

Best Model

Stochastic Gradient Descent (SGD)

Learning Rate = 0.1

import pandas as pd
import io
from IPython.display import Image, display

colab_path = '/content/drive/MyDrive/aml'

display(Image(colab_path + '/Pictures/Accuracy_SGD.png'))
display(Image(colab_path + '/Pictures/Loss_SGD.png'))
df2 = pd.read_csv(colab_path + '/Pictures/resultsAlexSGD1.csv')
print(df2)

    Unnamed: 0  train_loss  train_accuracy  val_loss  val_accuracy
0            0    0.692609        0.516440  0.691752      0.500247
1            1    0.690538        0.534148  0.695055      0.500247
2            2    0.689757        0.541196  0.691929      0.499753
3            3    0.690216        0.534341  0.684570      0.607514
4            4    0.680934        0.573188  0.661732      0.624320
5            5    0.673942        0.575854  0.656755      0.621354
6            6    0.661686        0.603402  0.683813      0.529906
7            7    0.651721        0.623969  0.690574      0.540287
8            8    0.644208        0.632982  0.663663      0.613445
9            9    0.641724        0.640599  0.641612      0.638655
10          10    0.626237        0.650248  0.645294      0.637173
11          11    0.643627        0.639584  0.609441      0.689570
12          12    0.616699        0.664339  0.567750      0.717746
13          13    0.585675        0.688714  0.556213      0.722195
14          14    0.571151        0.700647  0.680022      0.650025
15          15    0.565761        0.707249  0.513231      0.752348
16          16    0.547441        0.721341  0.511588      0.743945
17          17    0.548869        0.721975  0.491906      0.765694
18          18    0.527134        0.741145  0.481288      0.764706
19          19    0.494598        0.757268  0.489095      0.773109
20          20    0.496363        0.751809  0.471099      0.776075
21          21    0.452755        0.787610  0.445296      0.794365
22          22    0.457716        0.780754  0.397429      0.823529
23          23    0.437947        0.795227  0.405906      0.819575
24          24    0.413997        0.806830  0.502303      0.769155
25          25    0.441111        0.789768  0.401271      0.813149
26          26    0.391566        0.826457  0.376019      0.833416
27          27    0.390599        0.822833  0.401286      0.817598
28          28    0.372997        0.832170  0.351549      0.840830
29          29    0.372718        0.830011  0.369844      0.833910

Further Experiments

Stochastic Gradient Descent (SGD)

Learning Rate = 0.01

Testing out Adam optimizer did not achieve great results. I first implemented a learning rate of 0.01 but the loss values were so large it was not even worth displaying results. Setting the learning rate to 0.001, there was a slight improvement in loss but accuracy still barely got above 50% after 15 epochs.

display(Image(colab_path + '/Pictures/Accuracy_SGD2.png'))
display(Image(colab_path + '/Pictures/Loss_SGD2.png'))
df = pd.read_csv(colab_path + "/ResultsAlexSGD2.csv")
print(df)

   Unnamed: 0  train_loss  train_accuracy  val_loss  val_accuracy
0           0    0.691845        0.527358  0.690975      0.545230
1           1    0.691435        0.542973  0.690424      0.572912
2           2    0.691244        0.550590  0.690041      0.504202
3           3    0.690380        0.555795  0.689150      0.509639
4           4    0.689933        0.556303  0.687872      0.589224
5           5    0.688758        0.575346  0.686866      0.521008
6           6    0.687848        0.575854  0.684655      0.559071
7           7    0.686411        0.567475  0.681666      0.608502
8           8    0.685357        0.568110  0.679547      0.581315
9           9    0.682996        0.574076  0.674300      0.625803

Adam Optimizer

Learning Rate = 0.001

Testing out Adam optimizer did not achieve great results. I first implemented a learning rate of 0.01 but the loss values were so large it was not even worth displaying results. Setting the learning rate to 0.001, there was a slight improvement in loss but accuracy still barely got above 50% after 15 epochs.

display(Image(colab_path + '/Pictures/Accuracy_Adam.png'))
display(Image(colab_path + '/Pictures/Loss_Adam.png'))
df2 = pd.read_csv(colab_path + '/Pictures/resultsAdam.csv')
print(df2)

    Unnamed: 0  train_loss  train_accuracy  val_loss  val_accuracy
0            0    0.807833        0.508188  0.693017      0.499259
1            1    0.693115        0.505522  0.693454      0.499753
2            2    0.693362        0.494097  0.693151      0.499753
3            3    0.693269        0.493970  0.693148      0.500247
4            4    0.693300        0.488130  0.693154      0.500247
5            5    0.693350        0.501714  0.693157      0.500247
6            6    0.693214        0.503110  0.693224      0.499753
7            7    0.693450        0.491050  0.693152      0.499753
8            8    0.693194        0.502095  0.693180      0.499753
9            9    0.693187        0.504380  0.693147      0.499753
10          10    0.693168        0.499302  0.693148      0.499753
11          11    0.693201        0.495493  0.693148      0.500247
12          12    0.693250        0.495493  0.693148      0.499753
13          13    0.693278        0.495112  0.693147      0.500247
14          14    0.693143        0.501841  0.693147      0.500247

4.9.4 EfficientNet

EfficientNet classification models were utilized to predict if images contained cats or dogs. We accomplished this by performing transfer learning with EfficientNet models pre-trained on ImageNet. We froze the EfficientNet model and added a top layer to integrate the pre-trained model to our cat and dog dataset.

The following models were created:

1. EfficientNetB0
2. EfficientNetB0 with weight decay of 1e-6
3. EfficientNetB1 with weight decay of 1e-6
4. EfficientNetB2 with weight decay of 1e-6
5. EfficientNetB3 with weight decay of 1e-6
6. EfficientNetB4 with weight decay of 1e-6
7. EfficientNetB5 with weight decay of 1e-6

All EfficientNet models performed well with test accuracies above 97%. EfficientNet B0 is the base model and had the lowest test accuracy with a test accuracy of 97.1%. We added regularization by adding a weight decay of 1e-6 to the RMSProp optimizer. This led to a slight improvement in test accuracy for the EfficientNetB0 model, increasing the test accuracy from 97.1% to 97.4%. So we decided to implement the weight decay on all EfficientNet models going forward.

EfficientNet models implement compound scaling, meaning each model from EfficientNetB0 to EfficientNetB7 will increase in complexity with a set increase in depth of layer of 20%, width of 10%, and image resolution of 15%. The benefit of this compound scaling is evident with the test accuracy increasing with each EffcientNet model. EfficientNetB5 was the most complex model we were able to run and it reached the highest test accuracy score of 99.4%. A graph of the training and validation accurancy for each epoch is presented after each model. Each plot had a good fit with the training and validation accuracy increasing slightly with each epoch. This indicates that the model did not have an issue with overfitting.

Our best classification model in previous phases reached a test accuracy of 55.5%. This classification model was a simple neural network with only one hidden layer. In this phase we implemented a more complex CNN model which reached a test accuracy of 76%. Transfer learning with EfficientNet outperformed both of these classification models. The EfficientNet model had a more complex neural network and the pre-trained model had enough resources to train on a massive dataset (ImageNet). Both of these advantages led to better training accuracy performance.

Our team decided to move forward with EfficientNet instead of EfficientDet because of the ease of implementation with the Keras package, the ability to select a pre-trained model on the ImageNet dataset, and the capacity to use up to EfficientNetB5 without memory limitations. We were not able to implement the most complex models, EfficientNetB6 and EfficientNetB7, due to memory limitations.

4.9.5 Overall Results

Overall, the results were quite an increase from our previous phases. In phase 2, the best results were from the baseline classification model with a test accuracy of 59.4% and in phase 3, the best results were from the multilayer perceptron model (MLP) for classification with a test accuracy of 57%. We treated phase 3 as a learning opportunity for more complex models and applied these skills for our final submission. Taking in consideration these results, improvement from the previous best test accuracy yields a percent change of 74.4%.

4.10 Gap Analysis

Goal:

The goal of this project was to create optimal cat and dog image detection machine learning models. To accomplish this we have created classification models to predict cat or dog labels and regression pipelines to predict bounding boxes. Our success was measured through class scoreboard submissions and formal reports. This allowed us to compare our model performance to the rest of the class and receive feedback on how to improve our model implementations.

Current Status:

The top leader board scores for the cat and dog image detection project is posted below. We achieved the highest accuracy score for the groups working on this project. Our top performing model was EfficientNetB5 with a test accuracy score of 99.4%.

We accomplished this by performing transfer learning with EfficientNetB5 pre-trained on ImageNet. We froze the EfficientNetB5 model and added a top layer to integrate the pre-trained model to our cat and dog dataset. The image input resolution was 456, the total parameters in the neural network was 500,374,769, and the total run time was 20 minutes.

The second highest submission on the scoreboard was from Group 24. This group reported a test accuracy of 97% by completing transfer learning with the EfficientDet-D3 model. The image size was 896x896, the total parameters in the neural network was 12 million, and the total run time was 9 hours.

The EfficientDet model is similar to the EfficientNet model we completed. EfficientDet also uses compound scaling to uniformly scale the resolution, depth and width. In EfficientDet models, EfficientNet is the backbone network then a Weighted Bi-directional Feature Pyramid Network (BiFPN) is used for feature fusion.

Identify Gaps:

After researching, our team decided to move forward with the EfficientNet model instead of the EfficientDet model. A drawback of the EffiicentNet model is that it only does image classification tasks. The EfficientDet model is capable of performing classification and regression. In addition, the EfficientDet model which was released by Google in 2020 is the newest implementation of this type of network.

Our team implemented the EfficientNet model for two reasons. The EfficientNet model was released in 2019 and there are more examples of how to implement this model on various datasets. In addition, EfficientNetB0 to B7 can be easily implemented using Keras packages. This made freezing the model and building a top layer simple to implement.

Our team was also concerned about the limited resources we had to run models. We utilized google colab pro to run our models using GPU, but even with this resource we had issues with long run times and memory limitations in the past. We knew that we needed to select a model that was efficient and could achieve optimal performance with limited resources.

Develop Strategies for Closing Gaps:

Our team's strategy to improve the current top model would be to implement the state of the art EfficientDet model. With more time and resources we could freeze the EfficientDet model and build on the top layers to customize the model to our dataset. To account for resources and efficiency, we would limit the model to EfficenetD3. This strategy mergies the techniques applied by the two top performing cat and dog models.

4.11 Conclusion

In conclusion, we saw quite an improvement throughout phase 4 comparing our previous models. The best results yielded were from the EfficientNetB5 implementation with a 456 input resolution, and RMSprop optimizer. With AlexNet, there was a vast improvement from phase 3 to phase 4 but would require a larger training time and memory to achieve results that were given by EfficientNetB5. The FCN model was an improvement from previous phase implementation but since this is more for segmentation the results were surpassed by AlexNet and EfficientNetB5. CNN also can see an improvement from previous implementations but was also exceeded by Alexnet and EfficientNetB5.

Overall, it came as no surprise for EfficientNetB5 with RMSprop optimizer and the largest parameter size to outperform CNN, FCN and Alexnet. In order to achieve the best accuracy scores, this model was optimal to perform the cats and dogs detector task as it is currently one of the most powerful architectures for deep-learning classification in terms of processing time and results.

Bibliography

*Note: Any other reference might be within cell blocks along wide code.

Latex equations in Markdown: https://codesolid.com/using-latex-in-python/

Computations:

• Lab 4: Linear Regression
• Lab 6: Probability Theory Naive Bayes
• Lab 7: Logistic Regression Iris

Sources Keras CNN Classification Model

• https://www.w3schools.com/python/gloss_python_check_if_set_item_exists.asp

• https://www.geeksforgeeks.org/python-data-augmentation/

• https://gsurma.medium.com/image-classifier-cats-vs-dogs-with-convolutional-neural-networks-cnns-and-google-colabs-4e9af21ae7a8

• https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator

• https://studymachinelearning.com/keras-imagedatagenerator-with-flow/

• https://gsurma.medium.com/image-classifier-cats-vs-dogs-with-convolutional-neural-networks-cnns-and-google-colabs-4e9af21ae7a8

• https://keras.io/api/layers/convolution_layers/convolution2d/

• https://keras.io/api/layers/activations/

• https://www.tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy

• https://keras.io/api/layers/pooling_layers/max_pooling2d/

• https://wandb.ai/krishamehta/seo/reports/Difference-Between-SAME-and-VALID-Padding-in-TensorFlow--VmlldzoxODkwMzE

• https://keras.io/api/layers/reshaping_layers/flatten/

• https://machinelearningmastery.com/dropout-regularization-deep-learning-models-keras/

• https://gsurma.medium.com/image-classifier-cats-vs-dogs-with-convolutional-neural-networks-cnns-and-google-colabs-4e9af21ae7a8

• https://machinelearningmastery.com/display-deep-learning-model-training-history-in-keras/

• https://www.tensorflow.org/tensorboard/get_started

• https://machinelearningmastery.com/visualize-deep-learning-neural-network-model-keras/

• https://stackoverflow.com/questions/63684459/should-i-use-evaluate-generator-or-evaluate-to-evaluate-my-cnn-model

• https://www.tensorflow.org/guide/keras/train_and_evaluate

• https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax

Sources FCN

• https://towardsdatascience.com/implementing-a-fully-convolutional-network-fcn-in-tensorflow-2-3c46fb61de3b

• https://towardsdatascience.com/introduction-to-convolutional-neural-network-cnn-de73f69c5b83

• https://gsurma.medium.com/image-classifier-cats-vs-dogs-with-convolutional-neural-networks-cnns-and-google-colabs-4e9af21ae7a8

Sources EfficientNet

https://www.kaggle.com/code/arjunrao2000/beginners-guide-efficientnet-with-keras

https://www.analyticsvidhya.com/blog/2020/08/top-4-pre-trained-models-for-image-classification-with-python-code/

https://arxiv.org/pdf/1905.11946.pdf

https://medium.com/swlh/efficientnet-and-efficientdet-explained-297ff81a1267#:~:text=EfficientDet%20uses%20the%20same%20backbone,in%20multi%20scale%20feature%20fusion.

https://www.youtube.com/watch?v=GOxRSefbBoI&t=1522s

https://www.youtube.com/watch?v=RwQ-5v-kIck&t=624s

https://towardsdatascience.com/cifar-100-transfer-learning-using-efficientnet-ed3ed7b89af2

https://stackoverflow.com/questions/42443936/keras-split-train-test-set-when-using-imagedatagenerator

https://github.com/keras-team/keras/issues/5862#issuecomment-647559571

https://www.kaggle.com/code/arjunrao2000/beginners-guide-efficientnet-with-keras

https://stats.stackexchange.com/questions/309880/rescaling-neural-network-sigmoid-output-to-give-probability-of-binary-classifica

https://stackoverflow.com/questions/45806669/how-to-use-predict-generator-with-imagedatagenerator

https://iq.opengenus.org/efficientnet/ (diagram)

https://www.researchgate.net/figure/The-structure-of-an-EfficientNetB0-model-with-the-internal-structure-of-MBConv1-and_fig2_351057828 (diagram)

https://neptune.ai/blog/transfer-learning-guide-examples-for-images-and-text-in-keras

https://www.tensorflow.org/tutorials/images/transfer_learning

https://developer.ibm.com/articles/transfer-learning-for-deep-learning/

https://ai.googleblog.com/2019/05/efficientnet-improving-accuracy-and.html

https://keras.io/api/applications/efficientnet/

https://lessw.medium.com/efficientnet-from-google-optimally-scaling-cnn-model-architectures-with-compound-scaling-e094d84d19d4

https://medium.com/analytics-vidhya/efficientdet-scalable-and-efficient-object-detection-384a5df9011a

https://arxiv.org/abs/1911.09070

Sources AlexNet

https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf

https://towardsdatascience.com/alexnet-the-architecture-that-challenged-cnns-e406d5297951

Alexnet Input Shape: https://en.wikipedia.org/wiki/AlexNet

Alexnet Architecture: https://medium.com/analytics-vidhya/concept-of-alexnet-convolutional-neural-network-6e73b4f9ee30

https://medium.com/mlearning-ai/alexnet-and-image-classification-8cd8511548b4

https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/

https://www.kaggle.com/datasets/tongpython/cat-and-dog